Design of nodes for embedded and ultra low-power wireless sensor networks

NASA Astrophysics Data System (ADS)

Xu, Jun; You, Bo; Cui, Juan; Ma, Jing; Li, Xin

2008-10-01

Sensor network integrates sensor technology, MEMS (Micro-Electro-Mechanical system) technology, embedded computing, wireless communication technology and distributed information management technology. It is of great value to use it where human is quite difficult to reach. Power consumption and size are the most important consideration when nodes are designed for distributed WSN (wireless sensor networks). Consequently, it is of great importance to decrease the size of a node, reduce its power consumption and extend its life in network. WSN nodes have been designed using JN5121-Z01-M01 module produced by jennic company and IEEE 802.15.4/ZigBee technology. Its new features include support for CPU sleep modes and a long-term ultra low power sleep mode for the entire node. In low power configuration the node resembles existing small low power nodes. An embedded temperature sensor node has been developed to verify and explore our architecture. The experiment results indicate that the WSN has the characteristic of high reliability, good stability and ultra low power consumption.

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.

Predicting the behavior of techno-social systems.

PubMed

Vespignani, Alessandro

2009-07-24

We live in an increasingly interconnected world of techno-social systems, in which infrastructures composed of different technological layers are interoperating within the social component that drives their use and development. Examples are provided by the Internet, the World Wide Web, WiFi communication technologies, and transportation and mobility infrastructures. The multiscale nature and complexity of these networks are crucial features in understanding and managing the networks. The accessibility of new data and the advances in the theory and modeling of complex networks are providing an integrated framework that brings us closer to achieving true predictive power of the behavior of techno-social systems.

Implementing Smart School Technology at the Secondary Level.

ERIC Educational Resources Information Center

Stallard, Charles K.

This paper describes the characteristics of "smart schools" and offers guidelines for developing such schools. Smart schools are defined as having three features: (1) they are computer networked via local area networks in order to share information through teleconferencing, databases, and electronic mail; (2) they are connected beyond…

Surveillance Jumps on the Network

ERIC Educational Resources Information Center

Raths, David

2011-01-01

Internet protocol (IP) network-based cameras and digital video management software are maturing, and many issues that have surrounded them, including bandwidth, data storage, ease of use, and integration are starting to become clearer as the technology continues to evolve. Prices are going down and the number of features is going up. Many school…

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: The Fractal Dimensions of Complex Networks

NASA Astrophysics Data System (ADS)

Guo, Long; Cai, XU

2009-08-01

It is shown that many real complex networks share distinctive features, such as the small-world effect and the heterogeneous property of connectivity of vertices, which are different from random networks and regular lattices. Although these features capture the important characteristics of complex networks, their applicability depends on the style of networks. To unravel the universal characteristics many complex networks have in common, we study the fractal dimensions of complex networks using the method introduced by Shanker. We find that the average 'density' (ρ(r)) of complex networks follows a better power-law function as a function of distance r with the exponent df, which is defined as the fractal dimension, in some real complex networks. Furthermore, we study the relation between df and the shortcuts Nadd in small-world networks and the size N in regular lattices. Our present work provides a new perspective to understand the dependence of the fractal dimension df on the complex network structure.

OCTANET--an electronic library network: I. Design and development.

PubMed Central

Johnson, M F; Pride, R B

1983-01-01

The design and development of the OCTANET system for networking among medical libraries in the midcontinental region is described. This system's features and configuration may be attributed, at least in part, to normal evolution of technology in library networking, remote access to computers, and development of machine-readable data bases. Current functions and services of the system are outlined and implications for future developments in computer-based networking are discussed. PMID:6860825

A neural network ActiveX based integrated image processing environment.

PubMed

Ciuca, I; Jitaru, E; Alaicescu, M; Moisil, I

2000-01-01

The paper outlines an integrated image processing environment that uses neural networks ActiveX technology for object recognition and classification. The image processing environment which is Windows based, encapsulates a Multiple-Document Interface (MDI) and is menu driven. Object (shape) parameter extraction is focused on features that are invariant in terms of translation, rotation and scale transformations. The neural network models that can be incorporated as ActiveX components into the environment allow both clustering and classification of objects from the analysed image. Mapping neural networks perform an input sensitivity analysis on the extracted feature measurements and thus facilitate the removal of irrelevant features and improvements in the degree of generalisation. The program has been used to evaluate the dimensions of the hydrocephalus in a study for calculating the Evans index and the angle of the frontal horns of the ventricular system modifications.

Technology-Mediated ELT Writing: Acceptance and Engagement in an Online Moodle Course

ERIC Educational Resources Information Center

Zyad, Hicham

2016-01-01

In the past fifteen years, Web 2.0 social networking technologies have ushered in a new era of information production, distribution and consumption with significant implications for language teaching and learning. An example of such technology is Moodle, which is a learning management system with several useful features that can transform the…

2011 Technology Expo

NASA Image and Video Library

2011-05-26

Phillip Stallcup with Agilent Technologies in Huntsville, Ala., talks with NASA employees Leslie Ladner (l) and Kelly Sullivan about spectrum analyzers and other test equipment during the Stennis Technology Expo on May 26. The expo was hosted by NASA Solutions for Enterprise-Wide Procurement and featured various exhibitors demonstrating the latest in a range of technologies, such as training equipment, secure data storage, video networks, distance learning and data management.

Ethernet access network based on free-space optic deployment technology

NASA Astrophysics Data System (ADS)

Gebhart, Michael; Leitgeb, Erich; Birnbacher, Ulla; Schrotter, Peter

2004-06-01

The satisfaction of all communication needs from single households and business companies over a single access infrastructure is probably the most challenging topic in communications technology today. But even though the so-called "Last Mile Access Bottleneck" is well known since more than ten years and many distribution technologies have been tried out, the optimal solution has not yet been found and paying commercial access networks offering all service classes are still rare today. Conventional services like telephone, radio and TV, as well as new and emerging services like email, web browsing, online-gaming, video conferences, business data transfer or external data storage can all be transmitted over the well known and cost effective Ethernet networking protocol standard. Key requirements for the deployment technology driven by the different services are high data rates to the single customer, security, moderate deployment costs and good scalability to number and density of users, quick and flexible deployment without legal impediments and high availability, referring to the properties of optical and wireless communication. We demonstrate all elements of an Ethernet Access Network based on Free Space Optic distribution technology. Main physical parts are Central Office, Distribution Network and Customer Equipment. Transmission of different services, as well as configuration, service upgrades and remote control of the network are handled by networking features over one FSO connection. All parts of the network are proven, the latest commercially available technology. The set up is flexible and can be adapted to any more specific need if required.

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network.

PubMed

Guo, Hao; Qin, Mengna; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%.

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network

PubMed Central

Qin, Mengna; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%. PMID:29387141

A TTC upgrade proposal using bidirectional 10G-PON FTTH technology

NASA Astrophysics Data System (ADS)

Kolotouros, D. M.; Baron, S.; Soos, C.; Vasey, F.

2015-04-01

A new generation FPGA-based Timing-Trigger and Control (TTC) system based on emerging Passive Optical Network (PON) technology is being proposed to replace the existing off-detector TTC system used by the LHC experiments. High split ratio, dynamic software partitioning, low and deterministic latency, as well as low jitter are required. Exploiting the latest available technologies allows delivering higher capacity together with bidirectionality, a feature absent from the legacy TTC system. This article focuses on the features and capabilities of the latest TTC-PON prototype based on 10G-PON FTTH components along with some metrics characterizing its performance.

Satellite image analysis using neural networks

NASA Technical Reports Server (NTRS)

Sheldon, Roger A.

1990-01-01

The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.

Transmission in Optically Transparent Core Networks

NASA Astrophysics Data System (ADS)

Kilper, Dan; Jensen, Rich; Petermann, Klaus; Karasek, Miroslav

2007-03-01

Scope of Submission

• Optically transparent system design issues, transmission experiments, and field trials
• Optically transparent network architectures and topologies
• Dispersion management in reconfigurable and mesh systems
• Optically transparent network device and sub-system performance, design, characterization and control, including: amplifiers, transmitters, receivers, switches, add/drop multiplexers
• Transient and fault management
• Physical layer system control
• Monitoring and compensation to support transparency
• Wavelength routing and planning as they relate to physical layer transmission
• Hardware cost and configuration optimization for optically transparent networks

Through Technology Leverage and Risk Avoidance

ERIC Educational Resources Information Center

Sugasawa, Yoshio; Shinomiya, Takeshi

2010-01-01

Companies make concerted efforts to survive in a radically changing global society with the advent of a highly-networked and information-rich society that is featured by intense market competition. Manufacturing industries in particular have a tendency to rely on technological development strengths as a means of survival in a highly globalised and…

How long would SDH/SONET be prolonged?

NASA Astrophysics Data System (ADS)

Tao, Zhiyong; Mao, Qian

2004-04-01

As we all know, the increasing speed of data traffic is exceeding gradually from voice in today"s communication network. The main reason is the explosive of Internet. The controversy with IP over ATM/SDH/Optical becomes hotter and hotter, Many people in the telecommunication field are doubt: HOW LONG WOULD SDH/SONET BE PROLONGED? WHAT KIND OF SDH EQUIPMENTS COULD BE USED IN THE NETWORK? With the analysis from several aspects: services in the network, new development with SDH technology, market in transport equipment, This paper is considered that the SDH with some new features would be predominant transport technology in the recent years.

ACTS TDMA network control. [Advanced Communication Technology Satellite

NASA Technical Reports Server (NTRS)

Inukai, T.; Campanella, S. J.

1984-01-01

This paper presents basic network control concepts for the Advanced Communications Technology Satellite (ACTS) System. Two experimental systems, called the low-burst-rate and high-burst-rate systems, along with ACTS ground system features, are described. The network control issues addressed include frame structures, acquisition and synchronization procedures, coordinated station burst-time plan and satellite-time plan changes, on-board clock control based on ground drift measurements, rain fade control by means of adaptive forward-error-correction (FEC) coding and transmit power augmentation, and reassignment of channel capacities on demand. The NASA ground system, which includes a primary station, diversity station, and master control station, is also described.

Research and development of a NYNEX switched multi-megabit data service prototype system

NASA Astrophysics Data System (ADS)

Maman, K. H.; Haines, Robert; Chatterjee, Samir

1991-02-01

Switched Multi-megabit Data Service (SMDS) is a proposed high-speed packet-switched service which will support broadband applications such as Local Area Network (LAN) interconnections across a metropolitan area and beyond. This service is designed to take advantage of evolving Metropolitan Area Network (MAN) standards and technology which will provide customers with 45-mbps and 1 . 5-mbps access to high-speed public data communications networks. This paper will briefly discuss SMDS and review its architecture including the Subscriber Network Interface (SNI) and the SMDS Interface Protocol (SIP). It will review the fundamental features of SMDS such as address screening addressing scheme and access classes. Then it will describe the SMDS prototype system developed in-house by NYNEX Science Technology.

An overview on development of neural network technology

NASA Technical Reports Server (NTRS)

Lin, Chun-Shin

1993-01-01

The study has been to obtain a bird's-eye view of the current neural network technology and the neural network research activities in NASA. The purpose was two fold. One was to provide a reference document for NASA researchers who want to apply neural network techniques to solve their problems. Another one was to report out survey results regarding NASA research activities and provide a view on what NASA is doing, what potential difficulty exists and what NASA can/should do. In a ten week study period, we interviewed ten neural network researchers in the Langley Research Center and sent out 36 survey forms to researchers at the Johnson Space Center, Lewis Research Center, Ames Research Center and Jet Propulsion Laboratory. We also sent out 60 similar forms to educators and corporation researchers to collect general opinions regarding this field. Twenty-eight survey forms, 11 from NASA researchers and 17 from outside, were returned. Survey results were reported in our final report. In the final report, we first provided an overview on the neural network technology. We reviewed ten neural network structures, discussed the applications in five major areas, and compared the analog, digital and hybrid electronic implementation of neural networks. In the second part, we summarized known NASA neural network research studies and reported the results of the questionnaire survey. Survey results show that most studies are still in the development and feasibility study stage. We compared the techniques, application areas, researchers' opinions on this technology, and many aspects between NASA and non-NASA groups. We also summarized their opinions on difficulties encountered. Applications are considered the top research priority by most researchers. Hardware development and learning algorithm improvement are the next. The lack of financial and management support is among the difficulties in research study. All researchers agree that the use of neural networks could result in cost saving. Fault tolerance has been claimed as one important feature of neural computing. However, the survey indicates that very few studies address this issue. Fault tolerance is important in space mission and aircraft control. We believe that it is worthy for NASA to devote more efforts into the utilization of this feature.

Applying Web-Based Tools for Research, Engineering, and Operations

NASA Technical Reports Server (NTRS)

Ivancic, William D.

2011-01-01

Personnel in the NASA Glenn Research Center Network and Architectures branch have performed a variety of research related to space-based sensor webs, network centric operations, security and delay tolerant networking (DTN). Quality documentation and communications, real-time monitoring and information dissemination are critical in order to perform quality research while maintaining low cost and utilizing multiple remote systems. This has been accomplished using a variety of Internet technologies often operating simultaneously. This paper describes important features of various technologies and provides a number of real-world examples of how combining Internet technologies can enable a virtual team to act efficiently as one unit to perform advanced research in operational systems. Finally, real and potential abuses of power and manipulation of information and information access is addressed.

Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach.

PubMed

Tranchevent, Léon-Charles; Nazarov, Petr V; Kaoma, Tony; Schmartz, Georges P; Muller, Arnaud; Kim, Sang-Yoon; Rajapakse, Jagath C; Azuaje, Francisco

2018-06-07

One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients. We propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular, we observe that models built on our network-based approach perform at least as well as state of the art models. We furthermore explore the effectiveness of various topological features and observe, for instance, that redundant centrality metrics can be combined to build more powerful models. We demonstrate that the networks inferred from omics data contain clinically relevant information and that patient clinical outcomes can be predicted using only network topological data. This article was reviewed by Yang-Yu Liu, Tomislav Smuc and Isabel Nepomuceno.

Multiplex congruence network of natural numbers.

PubMed

Yan, Xiao-Yong; Wang, Wen-Xu; Chen, Guan-Rong; Shi, Ding-Hua

2016-03-31

Congruence theory has many applications in physical, social, biological and technological systems. Congruence arithmetic has been a fundamental tool for data security and computer algebra. However, much less attention was devoted to the topological features of congruence relations among natural numbers. Here, we explore the congruence relations in the setting of a multiplex network and unveil some unique and outstanding properties of the multiplex congruence network. Analytical results show that every layer therein is a sparse and heterogeneous subnetwork with a scale-free topology. Counterintuitively, every layer has an extremely strong controllability in spite of its scale-free structure that is usually difficult to control. Another amazing feature is that the controllability is robust against targeted attacks to critical nodes but vulnerable to random failures, which also differs from ordinary scale-free networks. The multi-chain structure with a small number of chain roots arising from each layer accounts for the strong controllability and the abnormal feature. The multiplex congruence network offers a graphical solution to the simultaneous congruences problem, which may have implication in cryptography based on simultaneous congruences. Our work also gains insight into the design of networks integrating advantages of both heterogeneous and homogeneous networks without inheriting their limitations.

Multiplex congruence network of natural numbers

NASA Astrophysics Data System (ADS)

Yan, Xiao-Yong; Wang, Wen-Xu; Chen, Guan-Rong; Shi, Ding-Hua

2016-03-01

Congruence theory has many applications in physical, social, biological and technological systems. Congruence arithmetic has been a fundamental tool for data security and computer algebra. However, much less attention was devoted to the topological features of congruence relations among natural numbers. Here, we explore the congruence relations in the setting of a multiplex network and unveil some unique and outstanding properties of the multiplex congruence network. Analytical results show that every layer therein is a sparse and heterogeneous subnetwork with a scale-free topology. Counterintuitively, every layer has an extremely strong controllability in spite of its scale-free structure that is usually difficult to control. Another amazing feature is that the controllability is robust against targeted attacks to critical nodes but vulnerable to random failures, which also differs from ordinary scale-free networks. The multi-chain structure with a small number of chain roots arising from each layer accounts for the strong controllability and the abnormal feature. The multiplex congruence network offers a graphical solution to the simultaneous congruences problem, which may have implication in cryptography based on simultaneous congruences. Our work also gains insight into the design of networks integrating advantages of both heterogeneous and homogeneous networks without inheriting their limitations.

The Space Mobile Network

NASA Technical Reports Server (NTRS)

Israel, David

2017-01-01

The definition and development of the next generation space communications and navigation architecture is underway. The primary goals are to remove communications and navigations constraints from missions and to enable increased autonomy. The Space Mobile Network (SMN) is an architectural concept that includes new technology and operations that will provide flight systems with an similar user experience to terrestrial wireless mobile networks. This talk will describe the SMN and its proposed new features, such as Disruption Tolerant Networking (DTN), optical communications, and User Initiated Services (UIS).

Call for Papers: Photonics in Switching

NASA Astrophysics Data System (ADS)

Wosinska, Lena; Glick, Madeleine

2006-04-01

Call for Papers: Photonics in Switching

Guest Editors:

Lena Wosinska, Royal Institute of Technology (KTH) / ICT Sweden Madeleine Glick, Intel Research, Cambridge, UK

Scope of Submission

• WDM node architectures
• Novel device technologies enabling photonics in switching, such as optical switch fabrics, optical memory, and wavelength conversion
• Routing protocols
• WDM switching and routing
• Quality of service
• Performance measurement and evaluation
• Next-generation optical networks: architecture, signaling, and control
• Traffic measurement and field trials
• Optical burst and packet switching
• OBS/OPS node architectures
• Burst/Packet scheduling and routing algorithms
• Contention resolution/avoidance strategies
• Services and applications for OBS/OPS (e.g., grid networks, storage-area networks, etc.)
• Burst assembly and ingress traffic shaping
• Hybrid OBS/TDM or OBS/wavelength routing

Manuscript Submission

Small Core, Big Network: A Comprehensive Approach to GIS Teaching Practice Based on Digital Three-Dimensional Campus Reconstruction

ERIC Educational Resources Information Center

Cheng, Liang; Zhang, Wen; Wang, Jiechen; Li, Manchun; Zhong, Lishan

2014-01-01

Geographic information science (GIS) features a wide range of disciplines and has broad applicability. Challenges associated with rapidly developing GIS technology and the currently limited teaching and practice materials hinder universities from cultivating highly skilled GIS graduates. Based on the idea of "small core, big network," a…

Hybrid Radio Frequency/Free-Space Optics (RF/FSO) Wireless Sensor Network: Security Concerns and Protective Measures

NASA Astrophysics Data System (ADS)

Banerjee, Koushik; Sharma, Hemant; Sengupta, Anasuya

Wireless sensor networks (WSNs) are ad hoc wireless networks that are written off as spread out structure and ad hoc deployment. Sensor networks have all the rudimentary features of ad hoc networks but to altered points—for instance, considerably lesser movement and far more energy necessities. Commonly used technology for communication is radio frequency (RF) communications. Free-space optics (FSO) is relatively new technology which has the prospective to deliver remarkable increases in network lifetime of WSN. Hybrid RF/FSO communications has been suggested to decrease power consumption by a single sensor node. It is observed that security plays a very important role for either RF WSN or hybrid RF/FSO WSN as those are vulnerable to numerous threats. In this paper, various possible attacks in RF/FSO WSN are discussed and aimed to propose some way out from those attacks.

Reconfigurable Robust Routing for Mobile Outreach Network

NASA Technical Reports Server (NTRS)

Lin, Ching-Fang

2010-01-01

The Reconfigurable Robust Routing for Mobile Outreach Network (R3MOO N) provides advanced communications networking technologies suitable for the lunar surface environment and applications. The R3MOON techn ology is based on a detailed concept of operations tailored for luna r surface networks, and includes intelligent routing algorithms and wireless mesh network implementation on AGNC's Coremicro Robots. The product's features include an integrated communication solution inco rporating energy efficiency and disruption-tolerance in a mobile ad h oc network, and a real-time control module to provide researchers an d engineers a convenient tool for reconfiguration, investigation, an d management.

Networking Biology: The Origins of Sequence-Sharing Practices in Genomics.

PubMed

Stevens, Hallam

2015-10-01

The wide sharing of biological data, especially nucleotide sequences, is now considered to be a key feature of genomics. Historians and sociologists have attempted to account for the rise of this sharing by pointing to precedents in model organism communities and in natural history. This article supplements these approaches by examining the role that electronic networking technologies played in generating the specific forms of sharing that emerged in genomics. The links between early computer users at the Stanford Artificial Intelligence Laboratory in the 1960s, biologists using local computer networks in the 1970s, and GenBank in the 1980s, show how networking technologies carried particular practices of communication, circulation, and data distribution from computing into biology. In particular, networking practices helped to transform sequences themselves into objects that had value as a community resource.

On detection and visualization techniques for cyber security situation awareness

NASA Astrophysics Data System (ADS)

Yu, Wei; Wei, Shixiao; Shen, Dan; Blowers, Misty; Blasch, Erik P.; Pham, Khanh D.; Chen, Genshe; Zhang, Hanlin; Lu, Chao

2013-05-01

Networking technologies are exponentially increasing to meet worldwide communication requirements. The rapid growth of network technologies and perversity of communications pose serious security issues. In this paper, we aim to developing an integrated network defense system with situation awareness capabilities to present the useful information for human analysts. In particular, we implement a prototypical system that includes both the distributed passive and active network sensors and traffic visualization features, such as 1D, 2D and 3D based network traffic displays. To effectively detect attacks, we also implement algorithms to transform real-world data of IP addresses into images and study the pattern of attacks and use both the discrete wavelet transform (DWT) based scheme and the statistical based scheme to detect attacks. Through an extensive simulation study, our data validate the effectiveness of our implemented defense system.

A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

NASA Astrophysics Data System (ADS)

Shuxin, Li; Zhilong, Zhang; Biao, Li

2018-01-01

Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

Multiobjective optimization of hybrid regenerative life support technologies. Topic D: Technology Assessment

NASA Technical Reports Server (NTRS)

Manousiouthakis, Vasilios

1995-01-01

We developed simple mathematical models for many of the technologies constituting the water reclamation system in a space station. These models were employed for subsystem optimization and for the evaluation of the performance of individual water reclamation technologies, by quantifying their operational 'cost' as a linear function of weight, volume, and power consumption. Then we performed preliminary investigations on the performance improvements attainable by simple hybrid systems involving parallel combinations of technologies. We are developing a software tool for synthesizing a hybrid water recovery system (WRS) for long term space missions. As conceptual framework, we are employing the state space approach. Given a number of available technologies and the mission specifications, the state space approach would help design flowsheets featuring optimal process configurations, including those that feature stream connections in parallel, series, or recycles. We visualize this software tool to function as follows: given the mission duration, the crew size, water quality specifications, and the cost coefficients, the software will synthesize a water recovery system for the space station. It should require minimal user intervention. The following tasks need to be solved for achieving this goal: (1) formulate a problem statement that will be used to evaluate the advantages of a hybrid WRS over a single technology WBS; (2) model several WRS technologies that can be employed in the space station; (3) propose a recycling network design methodology (since the WRS synthesis task is a recycling network design problem, it is essential to employ a systematic method in synthesizing this network); (4) develop a software implementation for this design methodology, design a hybrid system using this software, and compare the resulting WRS with a base-case WRS; and (5) create a user-friendly interface for this software tool.

Common solutions for power, communication and robustness in operations of large measurement networks within Research Infrastructures

NASA Astrophysics Data System (ADS)

Huber, Robert; Beranzoli, Laura; Fiebig, Markus; Gilbert, Olivier; Laj, Paolo; Mazzola, Mauro; Paris, Jean-Daniel; Pedersen, Helle; Stocker, Markus; Vitale, Vito; Waldmann, Christoph

2017-04-01

European Environmental Research Infrastructures (RI) frequently comprise in situ observatories from large-scale networks of platforms or sites to local networks of various sensors. Network operation is usually a cumbersome aspect of these RIs facing specific technological problems related to operations in remote areas, maintenance of the network, transmission of observation values, etc.. Robust inter-connection within and across these networks is still at infancy level and the burden increases with remoteness of the station, harshness of environmental conditions, and unavailability of classic communication systems, which is a common feature here. Despite existing RIs having developed ad-hoc solutions to overcome specific problems and innovative technologies becoming available, no common approach yet exists. Within the European project ENVRIplus, a dedicated work package aims to stimulate common network operation technologies and approaches in terms of power supply and storage, robustness, and data transmission. Major objectives of this task are to review existing technologies and RI requirements, propose innovative solutions and evaluate the standardization potential prior to wider deployment across networks. Focus areas within these efforts are: improving energy production and storage units, testing robustness of RI equipment towards extreme conditions as well as methodologies for robust data transmission. We will introduce current project activities which are coordinated at various levels including the engineering as well as the data management perspective, and explain how environmental RIs can benefit from the developments.

Development concepts of a Smart Cyber Operating Theater (SCOT) using ORiN technology.

PubMed

Okamoto, Jun; Masamune, Ken; Iseki, Hiroshi; Muragaki, Yoshihiro

2018-02-23

Currently, networking has not progressed in the treatment room. Almost every medical device in the treatment room operates as a stand-alone device. In this project, we aim to develop a networked operating room called "Smart Cyber Operating Theater (SCOT)". Medical devices are connected using Open Resource interface for the Network (ORiN) technology. In this paper, we describe the concept of the SCOT project. SCOT is integrated using the communication interface ORiN, which was originally developed for industry. One feature of ORiN is that the system can be constructed flexibly. ORiN creates abstracts of the same type of devices and increases the robustness of the system for device exchange. By using ORiN technology, we are developing new applications, such as decision-making navigation or a precision guided treatment system.

Optical Access Networks

NASA Astrophysics Data System (ADS)

Zheng, Jun; Ansari, Nirwan

2005-01-01

Call for Papers: Optical Access Networks

Guest Editors Jun Zheng, University of Ottawa Nirwan Ansari, New Jersey Institute of Technology

Submission Deadline: 1 June 2005

Background

Scope of Contributions

• Optical access network architectures and protocols
• Passive optical networks (BPON, EPON, GPON, etc.)
• Active optical networks
• Multiple access control
• Multiservices and QoS provisioning
• Network survivability
• Field trials and standards
• Performance modeling and analysis

Manuscript Submission

The Livermore Brain: Massive Deep Learning Networks Enabled by High Performance Computing

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

Chen, Barry Y.

The proliferation of inexpensive sensor technologies like the ubiquitous digital image sensors has resulted in the collection and sharing of vast amounts of unsorted and unexploited raw data. Companies and governments who are able to collect and make sense of large datasets to help them make better decisions more rapidly will have a competitive advantage in the information era. Machine Learning technologies play a critical role for automating the data understanding process; however, to be maximally effective, useful intermediate representations of the data are required. These representations or “features” are transformations of the raw data into a form where patternsmore » are more easily recognized. Recent breakthroughs in Deep Learning have made it possible to learn these features from large amounts of labeled data. The focus of this project is to develop and extend Deep Learning algorithms for learning features from vast amounts of unlabeled data and to develop the HPC neural network training platform to support the training of massive network models. This LDRD project succeeded in developing new unsupervised feature learning algorithms for images and video and created a scalable neural network training toolkit for HPC. Additionally, this LDRD helped create the world’s largest freely-available image and video dataset supporting open multimedia research and used this dataset for training our deep neural networks. This research helped LLNL capture several work-for-others (WFO) projects, attract new talent, and establish collaborations with leading academic and commercial partners. Finally, this project demonstrated the successful training of the largest unsupervised image neural network using HPC resources and helped establish LLNL leadership at the intersection of Machine Learning and HPC research.« less

Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks.

PubMed

Balaur, Irina; Mazein, Alexander; Saqi, Mansoor; Lysenko, Artem; Rawlings, Christopher J; Auffray, Charles

2017-04-01

The goal of this work is to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. Advanced user access features have been developed using the Neo4j graph database technology and this paper describes key features such as efficient management of the network data, examples of the network querying for addressing particular tasks, and how query results are converted back to the Systems Biology Markup Language (SBML) standard format. The Neo4j-based metabolic framework facilitates exploration of highly connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. A Java-based parser component has been developed to convert query results (available in the JSON format) into SBML and SIF formats in order to facilitate further results exploration, enhancement or network sharing. The Neo4j-based metabolic framework is freely available from: https://diseaseknowledgebase.etriks.org/metabolic/browser/ . The java code files developed for this work are available from the following url: https://github.com/ibalaur/MetabolicFramework . ibalaur@eisbm.org. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Recon2Neo4j: applying graph database technologies for managing comprehensive genome-scale networks

PubMed Central

Mazein, Alexander; Saqi, Mansoor; Lysenko, Artem; Rawlings, Christopher J.; Auffray, Charles

2017-01-01

Abstract Summary: The goal of this work is to offer a computational framework for exploring data from the Recon2 human metabolic reconstruction model. Advanced user access features have been developed using the Neo4j graph database technology and this paper describes key features such as efficient management of the network data, examples of the network querying for addressing particular tasks, and how query results are converted back to the Systems Biology Markup Language (SBML) standard format. The Neo4j-based metabolic framework facilitates exploration of highly connected and comprehensive human metabolic data and identification of metabolic subnetworks of interest. A Java-based parser component has been developed to convert query results (available in the JSON format) into SBML and SIF formats in order to facilitate further results exploration, enhancement or network sharing. Availability and Implementation: The Neo4j-based metabolic framework is freely available from: https://diseaseknowledgebase.etriks.org/metabolic/browser/. The java code files developed for this work are available from the following url: https://github.com/ibalaur/MetabolicFramework. Contact: ibalaur@eisbm.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27993779

Vein matching using artificial neural network in vein authentication systems

NASA Astrophysics Data System (ADS)

Noori Hoshyar, Azadeh; Sulaiman, Riza

2011-10-01

Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.

Mass storage technology in networks

NASA Astrophysics Data System (ADS)

Ishii, Katsunori; Takeda, Toru; Itao, Kiyoshi; Kaneko, Reizo

1990-08-01

Trends and features of mass storage subsystems in network are surveyed and their key technologies spotlighted. Storage subsystems are becoming increasingly important in new network systems in which communications and data processing are systematically combined. These systems require a new class of high-performance mass-information storage in order to effectively utilize their processing power. The requirements of high transfer rates, high transactional rates and large storage capacities, coupled with high functionality, fault tolerance and flexibility in configuration, are major challenges in storage subsystems. Recent progress in optical disk technology has resulted in improved performance of on-line external memories to optical disk drives, which are competing with mid-range magnetic disks. Optical disks are more effective than magnetic disks in using low-traffic random-access file storing multimedia data that requires large capacity, such as in archive use and in information distribution use by ROM disks. Finally, it demonstrates image coded document file servers for local area network use that employ 130mm rewritable magneto-optical disk subsystems.

Technology use and reasons to participate in social networking health websites among people living with HIV in the US.

PubMed

Horvath, Keith J; Danilenko, Gene P; Williams, Mark L; Simoni, Jane; Amico, K Rivet; Oakes, J Michael; Simon Rosser, B R

2012-05-01

Online social media and mobile technologies hold potential to enhance adherence to antiretroviral therapy (ART), although little is known about the current use of these technologies among people living with HIV (PLWH). To address this gap in understanding, 312 PLWH (84% male, 69% White) US adults completed an online survey in 2009, from which 22 persons accepted an invitation to participate in one of two online focus groups. Results showed that 76% of participants with lower ART adherence used social networking websites/features at least once a week. Their ideal online social networking health websites included one that facilitated socializing with others (45% of participants) and ones with relevant HIV informational content (22%), although privacy was a barrier to use (26%). Texting (81%), and to a lesser extent mobile web-access (51%), was widely used among participants. Results support the potential reach of online social networking and text messaging intervention approaches.

Towards the systematic development of medical networking technology.

PubMed

Faust, Oliver; Shetty, Ravindra; Sree, S Vinitha; Acharya, Sripathi; Acharya U, Rajendra; Ng, E Y K; Poo, Chua Kok; Suri, Jasjit

2011-12-01

Currently, there is a disparity in the availability of doctors between urban and rural areas of developing countries. Most experienced doctors and specialists, as well as advanced diagnostic technologies, are available in urban areas. People living in rural areas have less or sometimes even no access to affordable healthcare facilities. Increasing the number of doctors and charitable medical hospitals or deploying advanced medical technologies in these areas might not be economically feasible, especially in developing countries. We need to mobilize science and technology to master this complex, large scale problem in an objective, logical, and professional way. This can only be achieved with a collaborative effort where a team of experts works on both technical and non-technical aspects of this health care divide. In this paper we use a systems engineering framework to discuss hospital networks which might be solution for the problem. We argue that with the advancement in communication and networking technologies, economically middle class people and even some rural poor have access to internet and mobile communication systems. Thus, Hospital Digital Networking Technologies (HDNT), such as telemedicine, can be developed to utilize internet, mobile and satellite communication systems to connect primitive rural healthcare centers to well advanced modern urban setups and thereby provide better consultation and diagnostic care to the needy people. This paper describes requirements and limitations of the HDNTs. It also presents the features of telemedicine, the implementation issues and the application of wireless technologies in the field of medical networking.

Hierarchy Measure for Complex Networks

PubMed Central

Mones, Enys; Vicsek, Lilla; Vicsek, Tamás

2012-01-01

Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure. PMID:22470477

Online social networks that connect users to physical activity partners: a review and descriptive analysis.

PubMed

Nakhasi, Atul; Shen, Album Xiaotian; Passarella, Ralph Joseph; Appel, Lawrence J; Anderson, Cheryl Am

2014-06-16

The US Centers for Disease Control and Prevention have identified a lack of encouragement, support, or companionship from family and friends as a major barrier to physical activity. To overcome this barrier, online social networks are now actively leveraging principles of companion social support in novel ways. The aim was to evaluate the functionality, features, and usability of existing online social networks which seek to increase physical activity and fitness among users by connecting them to physical activity partners, not just online, but also face-to-face. In September 2012, we used 3 major databases to identify the website addresses for relevant online social networks. We conducted a Google search using 8 unique keyword combinations: the common keyword "find" coupled with 1 of 4 prefix terms "health," "fitness," "workout," or "physical" coupled with 1 of 2 stem terms "activity partners" or "activity buddies." We also searched 2 prominent technology start-up news sites, TechCrunch and Y Combinator, using 2 unique keyword combinations: the common keyword "find" coupled with 1 of 2 stem terms "activity partners" and "activity buddies." Sites were defined as online social health activity networks if they had the ability to (1) actively find physical activity partners or activities for the user, (2) offer dynamic, real-time tracking or sharing of social activities, and (3) provide virtual profiles to users. We excluded from our analysis sites that were not Web-based, publicly available, in English, or free. Of the 360 initial search results, we identified 13 websites that met our complete criteria of an online social health activity network. Features such as physical activity creation (13/13, 100%) and private messaging (12/13, 92%) appeared almost universally among these websites. However, integration with Web 2.0 technologies such as Facebook and Twitter (9/13, 69%) and the option of direct event joining (8/13, 62%) were not as universally present. Largely absent were more sophisticated features that would enable greater usability, such as interactive engagement prompts (3/13, 23%) and system-created best fit activities (3/13, 23%). Several major online social networks that connect users to physical activity partners currently exist and use standardized features to achieve their goals. Future research is needed to better understand how users utilize these features and how helpful they truly are.

Intellectual system for images restoration

NASA Astrophysics Data System (ADS)

Mardare, Igor

2005-02-01

Intelligence systems on basis of artificial neural networks and associative memory allow to solve effectively problems of recognition and restoration of images. However, within analytical technologies there are no dominating approaches of deciding of intellectual problems. Choice of the best technology depends on nature of problem, features of objects, volume of represented information about the object, number of classes of objects, etc. It is required to determine opportunities, preconditions and field of application of neural networks and associative memory for decision of problem of restoration of images and to use their supplementary benefits for further development of intelligence systems.

Implementation of aerial LiDAR technology to update highway feature inventory.

DOT National Transportation Integrated Search

2016-12-01

Highway assets, including traffic signs, traffic signals, light poles, and guardrails, are important components of : transportation networks. They guide, warn and protect drivers, and regulate traffic. To manage and maintain the : regular operation o...

The Third Annual NASA Science Internet User Working Group Conference

NASA Technical Reports Server (NTRS)

Lev, Brian S. (Editor); Gary, J. Patrick (Editor)

1993-01-01

The NASA Science Internet (NSI) User Support Office (USO) sponsored the Third Annual NSI User Working Group (NSIUWG) Conference March 30 through April 3, 1992, in Greenbelt, MD. Approximately 130 NSI users attended to learn more about the NSI, hear from projects which use NSI, and receive updates about new networking technologies and services. This report contains material relevant to the conference; copies of the agenda, meeting summaries, presentations, and descriptions of exhibitors. Plenary sessions featured a variety of speakers, including NSI project management, scientists, and NSI user project managers whose projects and applications effectively use NSI, and notable citizens of the larger Internet community. The conference also included exhibits of advanced networking applications; tutorials on internetworking, computer security, and networking technologies; and user subgroup meetings on the future direction of the conference, networking, and user services and applications.

Technology use and reasons to participate in online social networking websites for people living with HIV in the US

PubMed Central

Horvath, Keith J.; Danilenko, Gene P.; Williams, Mark L.; Simoni, Jane; Amico, K. Rivet; Oakes, J. Michael; Rosser, B.R. Simon

2012-01-01

It is unknown if online social networking technologies are already highly integrated among some people living with HIV (PLWH) or have yet to be adopted. To fill this gap in understanding, 312 PLWH (84% male, 69% white) residing in the US completed on online survey in 2009 of their patterns of social networking and mobile phone use. Twenty-two persons also participated in one of two online focus groups. Results showed that 76% of participants with lower adherence to HIV medication used social networking websites/features at least once a week. Their ideal online social networking health websites included one that facilitated socializing with others (45% of participants) and relevant informational content (22%), although privacy was a barrier to use (26%). Texting (81%), and to a lesser extent mobile web-access (51%), was widely used among participants. Results support the potential reach of online social networking and text messaging intervention approaches. PMID:22350832

Toward link predictability of complex networks

PubMed Central

Lü, Linyuan; Pan, Liming; Zhou, Tao; Zhang, Yi-Cheng; Stanley, H. Eugene

2015-01-01

The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in the consistency of structural features before and after a random removal of a small set of links. Based on the perturbation of the adjacency matrix, we propose a universal structural consistency index that is free of prior knowledge of network organization. Extensive experiments on disparate real-world networks demonstrate that (i) structural consistency is a good estimation of link predictability and (ii) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and robustness. This analysis has further applications in evaluating link prediction algorithms and monitoring sudden changes in evolving network mechanisms. It will provide unique fundamental insights into the above-mentioned academic research fields, and will foster the development of advanced information filtering technologies of interest to information technology practitioners. PMID:25659742

Wireless medical sensor networks: design requirements and enabling technologies.

PubMed

Vallejos de Schatz, Cecilia H; Medeiros, Henry Ponti; Schneider, Fabio K; Abatti, Paulo J

2012-06-01

This article analyzes wireless communication protocols that could be used in healthcare environments (e.g., hospitals and small clinics) to transfer real-time medical information obtained from noninvasive sensors. For this purpose the features of the three currently most widely used protocols-namely, Bluetooth(®) (IEEE 802.15.1), ZigBee (IEEE 802.15.4), and Wi-Fi (IEEE 802.11)-are evaluated and compared. The important features under consideration include data bandwidth, frequency band, maximum transmission distance, encryption and authentication methods, power consumption, and current applications. In addition, an overview of network requirements with respect to medical sensor features, patient safety and patient data privacy, quality of service, and interoperability between other sensors is briefly presented. Sensor power consumption is also discussed because it is considered one of the main obstacles for wider adoption of wireless networks in medical applications. The outcome of this assessment will be a useful tool in the hands of biomedical engineering researchers. It will provide parameters to select the most effective combination of protocols to implement a specific wireless network of noninvasive medical sensors to monitor patients remotely in the hospital or at home.

MAC layer security issues in wireless mesh networks

NASA Astrophysics Data System (ADS)

Reddy, K. Ganesh; Thilagam, P. Santhi

2016-03-01

Wireless Mesh Networks (WMNs) have emerged as a promising technology for a broad range of applications due to their self-organizing, self-configuring and self-healing capability, in addition to their low cost and easy maintenance. Securing WMNs is more challenging and complex issue due to their inherent characteristics such as shared wireless medium, multi-hop and inter-network communication, highly dynamic network topology and decentralized architecture. These vulnerable features expose the WMNs to several types of attacks in MAC layer. The existing MAC layer standards and implementations are inadequate to secure these features and fail to provide comprehensive security solutions to protect both backbone and client mesh. Hence, there is a need for developing efficient, scalable and integrated security solutions for WMNs. In this paper, we classify the MAC layer attacks and analyze the existing countermeasures. Based on attacks classification and countermeasures analysis, we derive the research directions to enhance the MAC layer security for WMNs.

Ontology patterns for complex topographic feature yypes

USGS Publications Warehouse

Varanka, Dalia E.

2011-01-01

Complex feature types are defined as integrated relations between basic features for a shared meaning or concept. The shared semantic concept is difficult to define in commonly used geographic information systems (GIS) and remote sensing technologies. The role of spatial relations between complex feature parts was recognized in early GIS literature, but had limited representation in the feature or coverage data models of GIS. Spatial relations are more explicitly specified in semantic technology. In this paper, semantics for topographic feature ontology design patterns (ODP) are developed as data models for the representation of complex features. In the context of topographic processes, component assemblages are supported by resource systems and are found on local landscapes. The topographic ontology is organized across six thematic modules that can account for basic feature types, resource systems, and landscape types. Types of complex feature attributes include location, generative processes and physical description. Node/edge networks model standard spatial relations and relations specific to topographic science to represent complex features. To demonstrate these concepts, data from The National Map of the U. S. Geological Survey was converted and assembled into ODP.

Many-body effects and excitonic features in 2D biphenylene carbon

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

Lüder, Johann, E-mail: johann.luder@physics.uu.se; Puglia, Carla; Eriksson, Olle

2016-01-14

The remarkable excitonic effects in low dimensional materials in connection to large binding energies of excitons are of great importance for research and technological applications such as in solar energy and quantum information processing as well as for fundamental investigations. In this study, the unique electronic and excitonic properties of the two dimensional carbon network biphenylene carbon were investigated with GW approach and the Bethe-Salpeter equation accounting for electron correlation effects and electron-hole interactions, respectively. Biphenylene carbon exhibits characteristic features including bright and dark excitons populating the optical gap of 0.52 eV and exciton binding energies of 530 meV asmore » well as a technologically relevant intrinsic band gap of 1.05 eV. Biphenylene carbon’s excitonic features, possibly tuned, suggest possible applications in the field of solar energy and quantum information technology in the future.« less

Clinical social networking--a new revolution in provider communication and delivery of clinical information across providers of care?

PubMed

Kolowitz, Brian J; Lauro, Gonzalo Romero; Venturella, James; Georgiev, Veliyan; Barone, Michael; Deible, Christopher; Shrestha, Rasu

2014-04-01

The adoption of social media technologies appears to enhance clinical outcomes through improved communications as reported by Bacigalupe (Fam Syst Heal 29(1):1-14, 2011). The ability of providers to more effectively, directly, and rapidly communicate among themselves as well as with patients should strengthen collaboration and treatment as reported by Bacigalupe (Fam Syst Heal 29(1):1-14, 2011). This paper is a case study in one organization's development of an internally designed and developed social technology solution termed "Unite." The Unite system combines social technologies' features including push notifications, messaging, community groups, and user lists with clinical workflow and applications to construct dynamic provider networks, simplify communications, and facilitate clinical workflow optimization. Modeling Unite as a social technology may ease adoption barriers. Developing a social network that is integrated with healthcare information systems in the clinical space opens the doors to capturing and studying the way in which providers communicate. The Unite system appears to have the potential to breaking down existing communication paradigms. With Unite, a rich set of usage data tied to clinical events may unravel alternative networks that can be leveraged to advance patient care.

Designing wireless sensor networks for hydrological and water resource applications: A purpose-oriented approach

NASA Astrophysics Data System (ADS)

Mao, F.; Hannah, D. M.; Krause, S.; Clark, J.; Buytaert, W.; Ochoa-Tocachi, B. F.

2017-12-01

There have been a growing number of studies using low-cost wireless sensor networks (LCWSNs) in hydrology and water resources fields. By reviewing the development of sensing and wireless communication technologies, as well as the recent relevant projects and applications, we observe that the challenges in applying LCWSNs have been moving beyond technical aspects. The large pool of available low-cost network modules, such as Arduino, Raspberry Pi, Xbee and inexpensive sensors, enable us to assemble networks rather than building them from scratch. With a wide variety of costs, functions and features, these modules support customisation of hydrological monitoring network for different user groups and purposes. Therefore, more attentions are needed to be placed on how to better design tailored LCWSNs with current technologies that create more added value for users. To address this challenge, this research proposes a tool-box for what we term `purpose-oriented' LCWSN. We identify the main LCWSN application scenarios from literature, and compare them from three perspectives including (1) the major stakeholders in each scenario, (2) the purposes for stakeholders, and (3) the network technologies and settings that meet the purposes. Notably, this innovative approach designs LCWSNs for different scenarios with considerations of not only technologies, but also stakeholders and purposes that are related to the usability, maintenance and social sustainability of networks. We conclude that this new, purpose-orientated approach can further release the potential of hydrological and water resources LCWSNs to maximise benefits for users and wider society.

Integration of hybrid wireless networks in cloud services oriented enterprise information systems

NASA Astrophysics Data System (ADS)

Li, Shancang; Xu, Lida; Wang, Xinheng; Wang, Jue

2012-05-01

This article presents a hybrid wireless network integration scheme in cloud services-based enterprise information systems (EISs). With the emerging hybrid wireless networks and cloud computing technologies, it is necessary to develop a scheme that can seamlessly integrate these new technologies into existing EISs. By combining the hybrid wireless networks and computing in EIS, a new framework is proposed, which includes frontend layer, middle layer and backend layers connected to IP EISs. Based on a collaborative architecture, cloud services management framework and process diagram are presented. As a key feature, the proposed approach integrates access control functionalities within the hybrid framework that provide users with filtered views on available cloud services based on cloud service access requirements and user security credentials. In future work, we will implement the proposed framework over SwanMesh platform by integrating the UPnP standard into an enterprise information system.

Electronic School. Supplement.

ERIC Educational Resources Information Center

American School Board Journal, 1997

1997-01-01

This supplementary insert describes developments in computer uses in education. Feature articles discuss connecting rural schools to computer networks through affordable wireless transmission, using the Internet to teach foreign languages, and forging links between the school and home through technology. Other columns discuss updates on the…

Maritime Geo-Fence Letter Report

DTIC Science & Technology

2016-07-01

Identification System ( AIS ). For the Arctic Technology Evaluation 2015 (ATE-15), the RDC utilized the CG Cutter HEALY (polar ice breaker) to...conduct testing of various AIS Transmit features to determine their utility for improving CG marine safety and security capabilities in the Arctic. During...ATE-15 three different operations were tested using AIS Technology. 1) Shore-to-Ship. The MXAK network of shore transmitters (three of which covered

Distributed architecture and distributed processing mode in urban sewage treatment

NASA Astrophysics Data System (ADS)

Zhou, Ruipeng; Yang, Yuanming

2017-05-01

Decentralized rural sewage treatment facility over the broad area, a larger operation and management difficult, based on the analysis of rural sewage treatment model based on the response to these challenges, we describe the principle, structure and function in networking technology and network communications technology as the core of distributed remote monitoring system, through the application of case analysis to explore remote monitoring system features in a decentralized rural sewage treatment facilities in the daily operation and management. Practice shows that the remote monitoring system to provide technical support for the long-term operation and effective supervision of the facilities, and reduced operating, maintenance and supervision costs for development.

A Network Scheduling Model for Distributed Control Simulation

NASA Technical Reports Server (NTRS)

Culley, Dennis; Thomas, George; Aretskin-Hariton, Eliot

2016-01-01

Distributed engine control is a hardware technology that radically alters the architecture for aircraft engine control systems. Of its own accord, it does not change the function of control, rather it seeks to address the implementation issues for weight-constrained vehicles that can limit overall system performance and increase life-cycle cost. However, an inherent feature of this technology, digital communication networks, alters the flow of information between critical elements of the closed-loop control. Whereas control information has been available continuously in conventional centralized control architectures through virtue of analog signaling, moving forward, it will be transmitted digitally in serial fashion over the network(s) in distributed control architectures. An underlying effect is that all of the control information arrives asynchronously and may not be available every loop interval of the controller, therefore it must be scheduled. This paper proposes a methodology for modeling the nominal data flow over these networks and examines the resulting impact for an aero turbine engine system simulation.

An Overview on Wireless Sensor Networks Technology and Evolution

PubMed Central

Buratti, Chiara; Conti, Andrea; Dardari, Davide; Verdone, Roberto

2009-01-01

Wireless sensor networks (WSNs) enable new applications and require non-conventional paradigms for protocol design due to several constraints. Owing to the requirement for low device complexity together with low energy consumption (i.e., long network lifetime), a proper balance between communication and signal/data processing capabilities must be found. This motivates a huge effort in research activities, standardization process, and industrial investments on this field since the last decade. This survey paper aims at reporting an overview of WSNs technologies, main applications and standards, features in WSNs design, and evolutions. In particular, some peculiar applications, such as those based on environmental monitoring, are discussed and design strategies highlighted; a case study based on a real implementation is also reported. Trends and possible evolutions are traced. Emphasis is given to the IEEE 802.15.4 technology, which enables many applications of WSNs. Some example of performance characteristics of 802.15.4-based networks are shown and discussed as a function of the size of the WSN and the data type to be exchanged among nodes. PMID:22423202

A novel biomedical image indexing and retrieval system via deep preference learning.

PubMed

Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

2018-05-01

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state-of-the-art techniques in indexing biomedical images. We propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications. Copyright © 2018 Elsevier B.V. All rights reserved.

Advanced ground station architecture

NASA Technical Reports Server (NTRS)

Zillig, David; Benjamin, Ted

1994-01-01

This paper describes a new station architecture for NASA's Ground Network (GN). The architecture makes efficient use of emerging technologies to provide dramatic reductions in size, operational complexity, and operational and maintenance costs. The architecture, which is based on recent receiver work sponsored by the Office of Space Communications Advanced Systems Program, allows integration of both GN and Space Network (SN) modes of operation in the same electronics system. It is highly configurable through software and the use of charged coupled device (CCD) technology to provide a wide range of operating modes. Moreover, it affords modularity of features which are optional depending on the application. The resulting system incorporates advanced RF, digital, and remote control technology capable of introducing significant operational, performance, and cost benefits to a variety of NASA communications and tracking applications.

Secured Hash Based Burst Header Authentication Design for Optical Burst Switched Networks

NASA Astrophysics Data System (ADS)

Balamurugan, A. M.; Sivasubramanian, A.; Parvathavarthini, B.

2017-12-01

The optical burst switching (OBS) is a promising technology that could meet the fast growing network demand. They are featured with the ability to meet the bandwidth requirement of applications that demand intensive bandwidth. OBS proves to be a satisfactory technology to tackle the huge bandwidth constraints, but suffers from security vulnerabilities. The objective of this proposed work is to design a faster and efficient burst header authentication algorithm for core nodes. There are two important key features in this work, viz., header encryption and authentication. Since the burst header is an important in optical burst switched network, it has to be encrypted; otherwise it is be prone to attack. The proposed MD5&RC4-4S based burst header authentication algorithm runs 20.75 ns faster than the conventional algorithms. The modification suggested in the proposed RC4-4S algorithm gives a better security and solves the correlation problems between the publicly known outputs during key generation phase. The modified MD5 recommended in this work provides 7.81 % better avalanche effect than the conventional algorithm. The device utilization result also shows the suitability of the proposed algorithm for header authentication in real time applications.

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

PubMed

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.

Feature extraction and identification in distributed optical-fiber vibration sensing system for oil pipeline safety monitoring

NASA Astrophysics Data System (ADS)

Wu, Huijuan; Qian, Ya; Zhang, Wei; Tang, Chenghao

2017-12-01

High sensitivity of a distributed optical-fiber vibration sensing (DOVS) system based on the phase-sensitivity optical time domain reflectometry (Φ-OTDR) technology also brings in high nuisance alarm rates (NARs) in real applications. In this paper, feature extraction methods of wavelet decomposition (WD) and wavelet packet decomposition (WPD) are comparatively studied for three typical field testing signals, and an artificial neural network (ANN) is built for the event identification. The comparison results prove that the WPD performs a little better than the WD for the DOVS signal analysis and identification in oil pipeline safety monitoring. The identification rate can be improved up to 94.4%, and the nuisance alarm rate can be effectively controlled as low as 5.6% for the identification network with the wavelet packet energy distribution features.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

PubMed Central

Peng, Yuan; Qiu, Mengran; Shi, Jianfei; Liu, Liangliang

2018-01-01

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved. PMID:29780407

Transitions from trees to cycles in adaptive flow networks

NASA Astrophysics Data System (ADS)

Martens, Erik A.; Klemm, Konstantin

2017-11-01

Transport networks are crucial to the functioning of natural and technological systems. Nature features transport networks that are adaptive over a vast range of parameters, thus providing an impressive level of robustness in supply. Theoretical and experimental studies have found that real-world transport networks exhibit both tree-like motifs and cycles. When the network is subject to load fluctuations, the presence of cyclic motifs may help to reduce flow fluctuations and, thus, render supply in the network more robust. While previous studies considered network topology via optimization principles, here, we take a dynamical systems approach and study a simple model of a flow network with dynamically adapting weights (conductances). We assume a spatially non-uniform distribution of rapidly fluctuating loads in the sinks and investigate what network configurations are dynamically stable. The network converges to a spatially non-uniform stable configuration composed of both cyclic and tree-like structures. Cyclic structures emerge locally in a transcritical bifurcation as the amplitude of the load fluctuations is increased. The resulting adaptive dynamics thus partitions the network into two distinct regions with cyclic and tree-like structures. The location of the boundary between these two regions is determined by the amplitude of the fluctuations. These findings may explain why natural transport networks display cyclic structures in the micro-vascular regions near terminal nodes, but tree-like features in the regions with larger veins.

Making the Connection.

ERIC Educational Resources Information Center

Renyi, Judith

1997-01-01

Designed by the National Foundation for the Improvement of education, Bill Gates's The Road Ahead Program features extensive Internet access for all students and teacher opportunities to work with networks and multimedia technologies. Each school in the 22 participating communities is paired with a community organization. Staff development should…

Modeling the adoption of innovations in the presence of geographic and media influences.

PubMed

Toole, Jameson L; Cha, Meeyoung; González, Marta C

2012-01-01

While there is a large body of work examining the effects of social network structure on innovation adoption, models to date have lacked considerations of real geography or mass media. In this article, we show these features are crucial to making more accurate predictions of a social contagion and technology adoption at a city-to-city scale. Using data from the adoption of the popular micro-blogging platform, Twitter, we present a model of adoption on a network that places friendships in real geographic space and exposes individuals to mass media influence. We show that homophily both among individuals with similar propensities to adopt a technology and geographic location is critical to reproducing features of real spatiotemporal adoption. Furthermore, we estimate that mass media was responsible for increasing Twitter's user base two to four fold. To reflect this strength, we extend traditional contagion models to include an endogenous mass media agent that responds to those adopting an innovation as well as influencing agents to adopt themselves.

Evaluation of Alternative Field Buses for Lighting ControlApplications

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

Koch, Ed; Rubinstein, Francis

2005-03-21

The Subcontract Statement of Work consists of two major tasks. This report is the Final Report in fulfillment of the contract deliverable for Task 1. The purpose of Task 1 was to evaluate existing and emerging protocols and standards for interfacing sensors and controllers for communicating with integrated lighting control systems in commercial buildings. The detailed task description follows: Task 1. Evaluate alternative sensor/field buses. The objective of this task is to evaluate existing and emerging standards for interfacing sensors and controllers for communicating with integrated lighting control systems in commercial buildings. The protocols to be evaluated will include atmore » least: (1) 1-Wire Net, (2) DALI, (3) MODBUS (or appropriate substitute such as EIB) and (4) ZigBee. The evaluation will include a comparative matrix for comparing the technical performance features of the different alternative systems. The performance features to be considered include: (1) directionality and network speed, (2) error control, (3) latency times, (4) allowable cable voltage drop, (5) topology, and (6) polarization. Specifically, Subcontractor will: (1) Analyze the proposed network architecture and identify potential problems that may require further research and specification. (2) Help identify and specify additional software and hardware components that may be required for the communications network to operate properly. (3) Identify areas of the architecture that can benefit from existing standards and technology and enumerate those standards and technologies. (4) Identify existing companies that may have relevant technology that can be applied to this research. (5) Help determine if new standards or technologies need to be developed.« less

Inferring general relations between network characteristics from specific network ensembles.

PubMed

Cardanobile, Stefano; Pernice, Volker; Deger, Moritz; Rotter, Stefan

2012-01-01

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.

Identifying and tracking attacks on networks: C3I displays and related technologies

NASA Astrophysics Data System (ADS)

Manes, Gavin W.; Dawkins, J.; Shenoi, Sujeet; Hale, John C.

2003-09-01

Converged network security is extremely challenging for several reasons; expanded system and technology perimeters, unexpected feature interaction, and complex interfaces all conspire to provide hackers with greater opportunities for compromising large networks. Preventive security services and architectures are essential, but in and of themselves do not eliminate all threat of compromise. Attack management systems mitigate this residual risk by facilitating incident detection, analysis and response. There are a wealth of attack detection and response tools for IP networks, but a dearth of such tools for wireless and public telephone networks. Moreover, methodologies and formalisms have yet to be identified that can yield a common model for vulnerabilities and attacks in converged networks. A comprehensive attack management system must coordinate detection tools for converged networks, derive fully-integrated attack and network models, perform vulnerability and multi-stage attack analysis, support large-scale attack visualization, and orchestrate strategic responses to cyber attacks that cross network boundaries. We present an architecture that embodies these principles for attack management. The attack management system described engages a suite of detection tools for various networking domains, feeding real-time attack data to a comprehensive modeling, analysis and visualization subsystem. The resulting early warning system not only provides network administrators with a heads-up cockpit display of their entire network, it also supports guided response and predictive capabilities for multi-stage attacks in converged networks.

Convergence

NASA Astrophysics Data System (ADS)

Darcie, Thomas E.; Doverspike, Robert; Zirngibl, Martin; Korotky, Steven K.

2005-01-01

Call for Papers: Convergence

Guest Editors: Thomas E. Darcie, University of Victoria Robert Doverspike, AT&T Martin Zirngibl, Lucent Technologies

Coordinating Associate Editor: Steven K. Korotky, Lucent Technologies

• Architecture, design and performance of optical wide-area-network (WAN), metro, and access networks
• Integration strategies for multiservice transport platforms
• Access methods that bridge traditional and emerging services
• Network signaling and control methodologies
• All-optical packet routing and switching techniques

Manuscript Submission

Distributed Telescope Networks in the Era of Network-Centric Astronomy

NASA Astrophysics Data System (ADS)

Solomos, N. H.

2010-07-01

In parallel with the world-wide demand for pushing our observational limits (increasingly larger telescope collecting power (ELTs) on the ground, most advanced technology satellites in space), we nowadays realize rapid rising of interest for the construction and deployment of a technologically advanced meta-network or Heterogeneous Telescope Network (hereafter HTN). The HTN is a Network of networks of telescopes and each node of it, consists of an inhomogeneous ensemble of different telescopes, sharing one common feature: the incorporation of a high degree of automation. The rationale behind this new tool, is that crucial astrophysical problems could be tackled very soon from the world-wide spread variety of well equipped autonomous telescopes working as a single instrument. In the full version of this paper, the research potential and future prospects of worldwide networked telescopic systems, is reviewed in the framework of current progress in Astrophysics. It is concluded that the research horizons of HTNs are very broad and the associated technology is currently in a maturity level that permits deployment. An extended interoperability-establishment initiative, involving telescopes of both hemispheres, based on accepted standards, appears a matter of priority. Observatories with infrastructure -of any size-, maintaining computerized telescope facilities, could respond to the challenge, devote part of their resources to the HTN and, in return, receive the rewards of shared resources, observing flexibility, optimized observing performance and the very high observing efficiency of a telescopic meta-network in facilitating competitive front line research.

On Proper Selection of Multihop Relays for Future Enhancement of AeroMACS Networks

NASA Technical Reports Server (NTRS)

Kamali, Behnam; Kerczewski, Robert J.; Apaza, Rafael D.

2015-01-01

As the Aeronautical Mobile Airport Communications System (AeroMACS) has evolved from a technology concept to a deployed communications network over major US airports, it is now time to contemplate whether the existing capacity of AeroMACS is sufficient to meet the demands set forth by all fixed and mobile applications over the airport surface given the AeroMACS constraints regarding bandwidth and transmit power. The underlying idea in this article is to present IEEE 802.16j-based WiMAX as a technology that can address future capacity enhancements and therefore is most feasible for AeroMACS applications. The principal argument in favor IEEE 802.16j technology is the flexible and cost effective extension of radio coverage that is afforded by relay fortified networks, with virtually no increase in the power requirements and virtually no rise in interference levels to co-allocated applications. The IEEE 802.16j-based multihop relay systems are briefly described. The focus is on key features of this technology, frame structure, and its architecture. Next, AeroMACS is described as a WiMAX-based wireless network. The two major relay modes supported by IEEE 802.16j amendment, i.e., transparent and non-transparent are described. The benefits of employing multihop relays are listed. Some key challenges related to incorporating relays into AeroMACS networks are discussed. The selection of relay type in a broadband wireless network affects a number of network parameters such as latency, signal overhead, PHY (Scalable Physical Layer) and MAC (Media Access Layer) layer protocols, consequently it can alter key network quantities of throughput and QoS (Quality of Service).

Using 100G Network Technology in Support of Petascale Science

NASA Technical Reports Server (NTRS)

Gary, James P.

2011-01-01

NASA in collaboration with a number of partners conducted a set of individual experiments and demonstrations during SC 10 that collectively were titled "Using 100G Network Technology in Support of Petascale Science". The partners included the iCAIR, Internet2, LAC, MAX, National LambdaRail (NLR), NOAA and SCinet Research Sandbox (SRS) as well as the vendors Ciena, Cisco, ColorChip, cPacket, Extreme Networks, Fusion-io, HP and Panduit who most generously allowed some of their leading edge 40G/100G optical transport, Ethernet switch and Internet Protocol router equipment and file server technologies to be involved. The experiments and demonstrations featured different vendor-provided 40G/100G network technology solutions for full-duplex 40G and 100G LAN data flows across SRS-deployed single-node fiber-pairs among the Exhibit Booths of NASA, the National Center for Data lining, NOAA and the SCinet Network Operations Center, as well as between the NASA Exhibit Booth in New Orleans and the Starlight Communications Exchange facility in Chicago across special SC 10- only 80- and 100-Gbps wide area network links provisioned respectively by the NLR and Internet2, then on to GSFC across a 40-Gbps link. provisioned by the Mid-Atlantic Crossroads. The networks and vendor equipment were load-stressed by sets of NASA/GSFC High End Computer Network Team-built, relatively inexpensive, net-test-workstations that are capable of demonstrating greater than 100Gbps uni-directional nuttcp-enabled memory-to-memory data transfers, greater than 80-Gbps aggregate--bidirectional memory-to-memory data transfers, and near 40-Gbps uni-directional disk-to-disk file copying. This paper will summarize the background context, key accomplishments and some significances of these experiments and demonstrations.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Transition Features from Simplicity-Universality to Complexity-Diversification Under UHNTF

NASA Astrophysics Data System (ADS)

Fang, Jin-Qing; Li, Yong

2010-02-01

A large unified hybrid network model with a variable speed growth (LUHNM-VSG) is proposed as third model of the unified hybrid network theoretical framework (UHNTF). A hybrid growth ratio vg of deterministic linking number to random linking number and variable speed growth index α are introduced in it. The main effects of vg and α on topological transition features of the LUHNM-VSG are revealed. For comparison with the other models, we construct a type of the network complexity pyramid with seven levels, in which from the bottom level-1 to the top level-7 of the pyramid simplicity-universality is increasing but complexity-diversity is decreasing. The transition relations between them depend on matching of four hybrid ratios (dr, fd, gr, vg). Thus the most of network models can be investigated in the unification way via four hybrid ratios (dr, fd, gr, vg). The LUHNM-VSG as the level-1 of the pyramid is much better and closer to description of real-world networks as well as has potential application.

Training Entrepreneurship at Universities: A Swedish Case.

ERIC Educational Resources Information Center

Klofsten, Magnus

2000-01-01

The Entrepreneurship and New Business Development Program trains Swedish individuals in the startup of technology- or knowledge-based enterprises. Built on the characteristics of entrepreneurial behavior, the program features a holistic outlook, a network of established entrepreneurs, mentoring, a mix of theory and practice, and focus on the…

Optical Ethernet

NASA Astrophysics Data System (ADS)

Chan, Calvin C. K.; Lam, Cedric F.; Tsang, Danny H. K.

2005-09-01

Call for Papers: Optical Ethernet The Journal of Optical Networking (JON) is soliciting papers for a second feature issue on Optical Ethernet. Ethernet has evolved from a LAN technology connecting desktop computers to a universal broadband network interface. It is not only the vehicle for local data connectivity but also the standard interface for next-generation network equipment such as video servers and IP telephony. High-speed Ethernet has been increasingly assuming the volume of backbone network traffic from SONET/SDH-based circuit applications. It is clear that IP has become the universal network protocol for future converged networks, and Ethernet is becoming the ubiquitous link layer for connectivity. Network operators have been offering Ethernet services for several years. Problems and new requirements in Ethernet service offerings have been captured through previous experience. New study groups and standards bodies have been formed to address these problems. This feature issue aims at reviewing and updating the new developments and R&D efforts of high-speed Ethernet in recent years, especially those related to the field of optical networking. Scope of Submission The scope of the papers includes, but is not limited to, the following: Ethernet PHY development 10-Gbit Ethernet on multimode fiber Native Ethernet transport and Ethernet on legacy networks EPON Ethernet OAM Resilient packet ring (RPR) and Ethernet QoS definition and management on Ethernet Ethernet protection switching Circuit emulation services on Ethernet Transparent LAN service development Carrier VLAN and Ethernet Ethernet MAC frame expansion Ethernet switching High-speed Ethernet applications Economic models of high-speed Ethernet services Ethernet field deployment and standard activities To submit to this special issue, follow the normal procedure for submission to JON, indicating "Optical Ethernet feature" in the "Comments" field of the online submission form. For all other questions relating to this feature issue, please send an e-mail to jon@osa.org, subject line "Optical Ethernet." Additional information can be found on the JON website: http://www.osa-jon.org/submission/

Emergence of structural patterns out of synchronization in networks with competitive interactions

NASA Astrophysics Data System (ADS)

Assenza, Salvatore; Gutiérrez, Ricardo; Gómez-Gardeñes, Jesús; Latora, Vito; Boccaletti, Stefano

2011-09-01

Synchronization is a collective phenomenon occurring in systems of interacting units, and is ubiquitous in nature, society and technology. Recent studies have enlightened the important role played by the interaction topology on the emergence of synchronized states. However, most of these studies neglect that real world systems change their interaction patterns in time. Here, we analyze synchronization features in networks in which structural and dynamical features co-evolve. The feedback of the node dynamics on the interaction pattern is ruled by the competition of two mechanisms: homophily (reinforcing those interactions with other correlated units in the graph) and homeostasis (preserving the value of the input strength received by each unit). The competition between these two adaptive principles leads to the emergence of key structural properties observed in real world networks, such as modular and scale-free structures, together with a striking enhancement of local synchronization in systems with no global order.

Deployment of the National Transparent Optical Network around the San Francisco Bay Area

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

McCammon, K.; Haigh, R.; Armstrong, G.

1996-06-01

We report on the deployment and initial operation of the National Transparent Optical Network, an experimental WDM network testbed around the San Francisco Bay Area, during the Optical Fiber Conference (OFC`96) held in San Jose, CA. The deployment aspects of the physical plant, optical and SONET layers are examined along with a discussion of broadband applications which utilized the network during the OFC`96 demonstration. The network features dense WDM technology, transparent optical routing technology using acousto- optic tunable filter based switches, and network modules with add/drop, multicast, and wavelength translation capabilities. The physical layer consisted of over 300 km ofmore » Sprint and Pacific Bell conventional single mode fiber which was amplified with I I optical amplifiers deployed in pre-amp, post-amp, and line amp configurations. An out-of-band control network provided datacom channels from remote equipment sites to the SONET network manager deployed at the San Jose Convention Center for the conference. Data transport over five wavelengths was achieved in the 1550 nm window using a variety of signal formats including analog and digital signal transmission on different wavelengths on the same fiber. The network operated throughout the week of OFC`96 and is still in operation today.« less

A new method for enhancer prediction based on deep belief network.

PubMed

Bu, Hongda; Gan, Yanglan; Wang, Yang; Zhou, Shuigeng; Guan, Jihong

2017-10-16

Studies have shown that enhancers are significant regulatory elements to play crucial roles in gene expression regulation. Since enhancers are unrelated to the orientation and distance to their target genes, it is a challenging mission for scholars and researchers to accurately predicting distal enhancers. In the past years, with the high-throughout ChiP-seq technologies development, several computational techniques emerge to predict enhancers using epigenetic or genomic features. Nevertheless, the inconsistency of computational models across different cell-lines and the unsatisfactory prediction performance call for further research in this area. Here, we propose a new Deep Belief Network (DBN) based computational method for enhancer prediction, which is called EnhancerDBN. This method combines diverse features, composed of DNA sequence compositional features, DNA methylation and histone modifications. Our computational results indicate that 1) EnhancerDBN outperforms 13 existing methods in prediction, and 2) GC content and DNA methylation can serve as relevant features for enhancer prediction. Deep learning is effective in boosting the performance of enhancer prediction.

Impact analysis of two kinds of failure strategies in Beijing road transportation network

NASA Astrophysics Data System (ADS)

Zhang, Zundong; Xu, Xiaoyang; Zhang, Zhaoran; Zhou, Huijuan

The Beijing road transportation network (BRTN), as a large-scale technological network, exhibits very complex and complicate features during daily periods. And it has been widely highlighted that how statistical characteristics (i.e. average path length and global network efficiency) change while the network evolves. In this paper, by using different modeling concepts, three kinds of network models of BRTN namely the abstract network model, the static network model with road mileage as weights and the dynamic network model with travel time as weights — are constructed, respectively, according to the topological data and the real detected flow data. The degree distribution of the three kinds of network models are analyzed, which proves that the urban road infrastructure network and the dynamic network behavior like scale-free networks. By analyzing and comparing the important statistical characteristics of three models under random attacks and intentional attacks, it shows that the urban road infrastructure network and the dynamic network of BRTN are both robust and vulnerable.

High-speed digital wireless battlefield network

NASA Astrophysics Data System (ADS)

Dao, Son K.; Zhang, Yongguang; Shek, Eddie C.; van Buer, Darrel

1999-07-01

In the past two years, the Digital Wireless Battlefield Network consortium that consists of HRL Laboratories, Hughes Network Systems, Raytheon, and Stanford University has participated in the DARPA TRP program to leverage the efforts in the development of commercial digital wireless products for use in the 21st century battlefield. The consortium has developed an infrastructure and application testbed to support the digitized battlefield. The consortium has implemented and demonstrated this network system. Each member is currently utilizing many of the technology developed in this program in commercial products and offerings. These new communication hardware/software and the demonstrated networking features will benefit military systems and will be applicable to the commercial communication marketplace for high speed voice/data multimedia distribution services.

Models and algorithm of optimization launch and deployment of virtual network functions in the virtual data center

NASA Astrophysics Data System (ADS)

Bolodurina, I. P.; Parfenov, D. I.

2017-10-01

The goal of our investigation is optimization of network work in virtual data center. The advantage of modern infrastructure virtualization lies in the possibility to use software-defined networks. However, the existing optimization of algorithmic solutions does not take into account specific features working with multiple classes of virtual network functions. The current paper describes models characterizing the basic structures of object of virtual data center. They including: a level distribution model of software-defined infrastructure virtual data center, a generalized model of a virtual network function, a neural network model of the identification of virtual network functions. We also developed an efficient algorithm for the optimization technology of containerization of virtual network functions in virtual data center. We propose an efficient algorithm for placing virtual network functions. In our investigation we also generalize the well renowned heuristic and deterministic algorithms of Karmakar-Karp.

Application of Mobile-ip to Space and Aeronautical Networks

NASA Technical Reports Server (NTRS)

Leung, Kent; Shell, Dan; Ivancic, William D.; Stewart, David H.; Bell, Terry L.; Kachmar, Brian A.

2001-01-01

The National Aeronautics and Space Administration (NASA) is interested in applying mobile Internet protocol (mobile-ip) technologies to its space and aeronautics programs. In particular, mobile-ip will play a major role in the Advanced Aeronautic Transportation Technology (AAT-F), the Weather Information Communication (WINCOMM), and the Small Aircraft Transportation System (SATS) aeronautics programs. This paper describes mobile-ip and mobile routers--in particular, the features, capabilities, and initial performance of the mobile router are presented. The application of mobile-router technology to NASA's space and aeronautics programs is also discussed.

Reconfigurable microfluidic hanging drop network for multi-tissue interaction and analysis.

PubMed

Frey, Olivier; Misun, Patrick M; Fluri, David A; Hengstler, Jan G; Hierlemann, Andreas

2014-06-30

Integration of multiple three-dimensional microtissues into microfluidic networks enables new insights in how different organs or tissues of an organism interact. Here, we present a platform that extends the hanging-drop technology, used for multi-cellular spheroid formation, to multifunctional complex microfluidic networks. Engineered as completely open, 'hanging' microfluidic system at the bottom of a substrate, the platform features high flexibility in microtissue arrangements and interconnections, while fabrication is simple and operation robust. Multiple spheroids of different cell types are formed in parallel on the same platform; the different tissues are then connected in physiological order for multi-tissue experiments through reconfiguration of the fluidic network. Liquid flow is precisely controlled through the hanging drops, which enable nutrient supply, substance dosage and inter-organ metabolic communication. The possibility to perform parallelized microtissue formation on the same chip that is subsequently used for complex multi-tissue experiments renders the developed platform a promising technology for 'body-on-a-chip'-related research.

Development of Medical Technology for Contingency Response to Marrow Toxic Agents

DTIC Science & Technology

2012-01-27

participated in the following activities supporting the Radiation Injury Treatment Network: o He was a featured speaker on the response to the Fukushima ...... Daiichi nuclear power plant incident a manuscript in preparation for Lancet on response to radiation incidents o He assisted with the 2011 update of

The wireless networking system of Earthquake precursor mobile field observation

NASA Astrophysics Data System (ADS)

Wang, C.; Teng, Y.; Wang, X.; Fan, X.; Wang, X.

2012-12-01

The mobile field observation network could be real-time, reliably record and transmit large amounts of data, strengthen the physical signal observations in specific regions and specific period, it can improve the monitoring capacity and abnormal tracking capability. According to the features of scatter everywhere, a large number of current earthquake precursor observation measuring points, networking technology is based on wireless broadband accessing McWILL system, the communication system of earthquake precursor mobile field observation would real-time, reliably transmit large amounts of data to the monitoring center from measuring points through the connection about equipment and wireless accessing system, broadband wireless access system and precursor mobile observation management center system, thereby implementing remote instrument monitoring and data transmition. At present, the earthquake precursor field mobile observation network technology has been applied to fluxgate magnetometer array geomagnetic observations of Tianzhu, Xichang,and Xinjiang, it can be real-time monitoring the working status of the observational instruments of large area laid after the last two or three years, large scale field operation. Therefore, it can get geomagnetic field data of the local refinement regions and provide high-quality observational data for impending earthquake tracking forecast. Although, wireless networking technology is very suitable for mobile field observation with the features of simple, flexible networking etc, it also has the phenomenon of packet loss etc when transmitting a large number of observational data due to the wireless relatively weak signal and narrow bandwidth. In view of high sampling rate instruments, this project uses data compression and effectively solves the problem of data transmission packet loss; Control commands, status data and observational data transmission use different priorities and means, which control the packet loss rate within an acceptable range and do not affect real-time observation curve. After field running test and earthquake tracking project applications, the field mobile observation wireless networking system is operate normally, various function have good operability and show good performance, the quality of data transmission meet the system design requirements and play a significant role in practical applications.

Consumer-identified barriers and strategies for optimizing technology use in the workplace.

PubMed

De Jonge, Desleigh M; Rodger, Sylvia A

2006-01-01

This article explores the experiences of 26 assistive technology (AT) users having a range of physical impairments as they optimized their use of technology in the workplace. A qualitative research design was employed using in-depth, open-ended interviews and observations of AT users in the workplace. Participants identified many factors that limited their use of technology such as discomfort and pain, limited knowledge of the technology's features, and the complexity of the technology. The amount of time required for training, limited work time available for mastery, cost of training and limitations of the training provided, resulted in an over-reliance on trial and error and informal support networks and a sense of isolation. AT users enhanced their use of technology by addressing the ergonomics of the workstation and customizing the technology to address individual needs and strategies. Other key strategies included tailored training and learning support as well as opportunities to practice using the technology and explore its features away from work demands. This research identified structures important for effective AT use in the workplace which need to be put in place to ensure that AT users are able to master and optimize their use of technology.

Asynchronous Transfer Mode (ATM) Switch Technology and Vendor Survey

NASA Technical Reports Server (NTRS)

Berry, Noemi

1995-01-01

Asynchronous Transfer Mode (ATM) switch and software features are described and compared in order to make switch comparisons meaningful. An ATM switch's performance cannot be measured solely based on its claimed switching capacity; traffic management and congestion control are emerging as the determining factors in an ATM network's ultimate throughput. Non-switch ATM products and experiences with actual installations of ATM networks are described. A compilation of select vendor offerings as of October 1994 is provided in chart form.

Designing of routing algorithms in autonomous distributed data transmission system for mobile computing devices with ‘WiFi-Direct’ technology

NASA Astrophysics Data System (ADS)

Nikitin, I. A.; Sherstnev, V. S.; Sherstneva, A. I.; Botygin, I. A.

2017-02-01

The results of the research of existent routing protocols in wireless networks and their main features are discussed in the paper. Basing on the protocol data, the routing protocols in wireless networks, including search routing algorithms and phone directory exchange algorithms, are designed with the ‘WiFi-Direct’ technology. Algorithms without IP-protocol were designed, and that enabled one to increase the efficiency of the algorithms while working only with the MAC-addresses of the devices. The developed algorithms are expected to be used in the mobile software engineering with the Android platform taken as base. Easier algorithms and formats of the well-known route protocols, rejection of the IP-protocols enables to use the developed protocols on more primitive mobile devices. Implementation of the protocols to the engineering industry enables to create data transmission networks among working places and mobile robots without any access points.

Optical CDMA components requirements

NASA Astrophysics Data System (ADS)

Chan, James K.

1998-08-01

Optical CDMA is a complementary multiple access technology to WDMA. Optical CDMA potentially provides a large number of virtual optical channels for IXC, LEC and CLEC or supports a large number of high-speed users in LAN. In a network, it provides asynchronous, multi-rate, multi-user communication with network scalability, re-configurability (bandwidth on demand), and network security (provided by inherent CDMA coding). However, optical CDMA technology is less mature in comparison to WDMA. The components requirements are also different from WDMA. We have demonstrated a video transport/switching system over a distance of 40 Km using discrete optical components in our laboratory. We are currently pursuing PIC implementation. In this paper, we will describe the optical CDMA concept/features, the demonstration system, and the requirements of some critical optical components such as broadband optical source, broadband optical amplifier, spectral spreading/de- spreading, and fixed/programmable mask.

Scalable Lunar Surface Networks and Adaptive Orbit Access

NASA Technical Reports Server (NTRS)

Wang, Xudong

2015-01-01

Teranovi Technologies, Inc., has developed innovative network architecture, protocols, and algorithms for both lunar surface and orbit access networks. A key component of the overall architecture is a medium access control (MAC) protocol that includes a novel mechanism of overlaying time division multiple access (TDMA) and carrier sense multiple access with collision avoidance (CSMA/CA), ensuring scalable throughput and quality of service. The new MAC protocol is compatible with legacy Institute of Electrical and Electronics Engineers (IEEE) 802.11 networks. Advanced features include efficiency power management, adaptive channel width adjustment, and error control capability. A hybrid routing protocol combines the advantages of ad hoc on-demand distance vector (AODV) routing and disruption/delay-tolerant network (DTN) routing. Performance is significantly better than AODV or DTN and will be particularly effective for wireless networks with intermittent links, such as lunar and planetary surface networks and orbit access networks.

LMS in university for in-class education: Synergy of free software, competitive approach and social networks technology

NASA Astrophysics Data System (ADS)

Radygin, V. Y.; Lukyanova, N. V.; Kupriyanov, D. Yu.

2017-01-01

Transformation of learning management systems over last two decades was investigated. The features of using e-learning systems for in-class education were discussed. The necessity of integration e-learning system with the student performance controlling system was shown. The influence of choice of student ranking system on students' motivation was described. The own way to choice of e-learning system design principles and technologies were suggested.

Arctic Glass: Innovative Consumer Technology in Support of Arctic Research

NASA Astrophysics Data System (ADS)

Ruthkoski, T.

2015-12-01

The advancement of cyberinfrastructure on the North Slope of Alaska is drastically limited by location-specific conditions, including: unique geophysical features, remoteness of location, and harsh climate. The associated cost of maintaining this unique cyberinfrastructure also becomes a limiting factor. As a result, field experiments conducted in this region have historically been at a technological disadvantage. The Arctic Glass project explored a variety of scenarios where innovative consumer-grade technology was leveraged as a lightweight, rapidly deployable, sustainable, alternatives to traditional large-scale Arctic cyberinfrastructure installations. Google Glass, cloud computing services, Internet of Things (IoT) microcontrollers, miniature LIDAR, co2 sensors designed for HVAC systems, and portable network kits are several of the components field-tested at the Toolik Field Station as part of this project. Region-specific software was also developed, including a multi featured, voice controlled Google Glass application named "Arctic Glass". Additionally, real-time sensor monitoring and remote control capability was evaluated through the deployment of a small cluster of microcontroller devices. Network robustness was analyzed as the devices delivered streams of abiotic data to a web-based dashboard monitoring service in near real time. The same data was also uploaded synchronously by the devices to Amazon Web Services. A detailed overview of solutions deployed during the 2015 field season, results from experiments utilizing consumer sensors, and potential roles consumer technology could play in support of Arctic science will be discussed.

Chinese character recognition based on Gabor feature extraction and CNN

NASA Astrophysics Data System (ADS)

Xiong, Yudian; Lu, Tongwei; Jiang, Yongyuan

2018-03-01

As an important application in the field of text line recognition and office automation, Chinese character recognition has become an important subject of pattern recognition. However, due to the large number of Chinese characters and the complexity of its structure, there is a great difficulty in the Chinese character recognition. In order to solve this problem, this paper proposes a method of printed Chinese character recognition based on Gabor feature extraction and Convolution Neural Network(CNN). The main steps are preprocessing, feature extraction, training classification. First, the gray-scale Chinese character image is binarized and normalized to reduce the redundancy of the image data. Second, each image is convoluted with Gabor filter with different orientations, and the feature map of the eight orientations of Chinese characters is extracted. Third, the feature map through Gabor filters and the original image are convoluted with learning kernels, and the results of the convolution is the input of pooling layer. Finally, the feature vector is used to classify and recognition. In addition, the generalization capacity of the network is improved by Dropout technology. The experimental results show that this method can effectively extract the characteristics of Chinese characters and recognize Chinese characters.

Development Status of the Rad-Tolerant TTEthernet Controller

NASA Astrophysics Data System (ADS)

Fidi, Christian; van Masar, Ivan

2016-08-01

The use of switched networking technologies for aerospace and more recently automotive brings additional advantages for space applications like the increase in performance of the overall avionics of a spacecraft. These networks are characterized by a central device (switch) and a point-to-point structure between switch and terminal devices that eases electrical and logical insulation.However, for a use in highly-reliable or highly-available applications as in launchers or satellites systems, these network technologies need to provide built-in determinism and redundancy to fulfill the tight latency and jitter requirements of the avionics control loops and the respective hardware redundancy. Therefore a state of the art networking technology already provides these features and allows the modularity and scalability to be used for the different space applications and would allow combining the deterministic avionics with the high speed payload network in a spacecraft [1].Introducing the time-triggered principle to Ethernet allows combining the open industry standard IEE802.3 Ethernet currently use in almost all GSE platforms, with full control of latency and jitter of the time-triggered approach. To allow the time-triggered data flow over Ethernet, a network- wide synchronization time-base has to be established to allow deriving all network events on a globally known time which is typically done in software in almost all spacecrafts. The additional synchronization service of Time-triggered Ethernet has been implemented as additional quality of service (QoS) on layer 2 of the ISO/OSI network model and been standardized in the SAE AS6802 [3].Within a launcher, the communication system ensured the data exchanges between avionic functions during all phases of the launcher lifecycle which is composed of three areas: AIT operations, ground phase and flight phase. To ensure the use of a single network for the different phases, the network needs to support features like the handling of different traffic classes (critical traffic and non-critical traffic, i.e. TT, RC and BE [2]). Also the compatibility to the IEEE1588 synchronization protocol can be used to connect legacy IEEE1588 equipment for GSE equipment.However this commercially available technology currently used in the aviation-, the industrial- and the automotive market needs to be matured for the use in space applications. Therefore a development of the necessary space-grade components, mainly the switch and the end system is needed.This paper presents the current development status of a radiation tolerant integrated circuit for the use in different space applications. It outlines the different steps needed to be performed to ensure the usability of this digital chip in highly reliable as well as in highly available space applications.

Deep Space Network information system architecture study

NASA Technical Reports Server (NTRS)

Beswick, C. A.; Markley, R. W. (Editor); Atkinson, D. J.; Cooper, L. P.; Tausworthe, R. C.; Masline, R. C.; Jenkins, J. S.; Crowe, R. A.; Thomas, J. L.; Stoloff, M. J.

1992-01-01

The purpose of this article is to describe an architecture for the Deep Space Network (DSN) information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990s. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies, such as the following: computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.

On the origins of hierarchy in complex networks

PubMed Central

Corominas-Murtra, Bernat; Goñi, Joaquín; Solé, Ricard V.; Rodríguez-Caso, Carlos

2013-01-01

Hierarchy seems to pervade complexity in both living and artificial systems. Despite its relevance, no general theory that captures all features of hierarchy and its origins has been proposed yet. Here we present a formal approach resulting from the convergence of theoretical morphology and network theory that allows constructing a 3D morphospace of hierarchies and hence comparing the hierarchical organization of ecological, cellular, technological, and social networks. Embedded within large voids in the morphospace of all possible hierarchies, four major groups are identified. Two of them match the expected from random networks with similar connectivity, thus suggesting that nonadaptive factors are at work. Ecological and gene networks define the other two, indicating that their topological order is the result of functional constraints. These results are consistent with an exploration of the morphospace, using in silico evolved networks. PMID:23898177

Classifying patents based on their semantic content.

PubMed

Bergeaud, Antonin; Potiron, Yoann; Raimbault, Juste

2017-01-01

In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information.

Portable Computer Technology (PCT) Research and Development Program Phase 2

NASA Technical Reports Server (NTRS)

Castillo, Michael; McGuire, Kenyon; Sorgi, Alan

1995-01-01

The subject of this project report, focused on: (1) Design and development of two Advanced Portable Workstation 2 (APW 2) units. These units incorporate advanced technology features such as a low power Pentium processor, a high resolution color display, National Television Standards Committee (NTSC) video handling capabilities, a Personal Computer Memory Card International Association (PCMCIA) interface, and Small Computer System Interface (SCSI) and ethernet interfaces. (2) Use these units to integrate and demonstrate advanced wireless network and portable video capabilities. (3) Qualification of the APW 2 systems for use in specific experiments aboard the Mir Space Station. A major objective of the PCT Phase 2 program was to help guide future choices in computing platforms and techniques for meeting National Aeronautics and Space Administration (NASA) mission objectives. The focus being on the development of optimal configurations of computing hardware, software applications, and network technologies for use on NASA missions.

Classifying patents based on their semantic content

PubMed Central

2017-01-01

In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information. PMID:28445550

100 Gb/s optical discrete multi-tone transceivers for intra- and inter-datacenter networks

NASA Astrophysics Data System (ADS)

Okabe, Ryo; Tanaka, Toshiki; Nishihara, Masato; Kai, Yutaka; Takahara, Tomoo; Liu, Bo; Li, Lei; Tao, Zhenning; Rasmussen, Jens C.

2016-03-01

Discrete multi-tone (DMT) technology is an attractive modulation technology for short-reach application due to its high spectral efficiency and simple configuration. In this paper, we first explain the features of DMT technology then discuss the impact of fiber dispersion and chirp on the frequency responses of the DMT signal and the importance in the relationship between chirp and the optical transmission band. Next, we explain our experiments of 100-Gb/s DMT transmission of 10 km in the O-band using directly modulated lasers for low-cost application. In an inter-datacenter network of more than several tens of kilometers, fiber dispersion mainly limits system performance. We also discuss our experiment of 100-Gb/s DMT transmission up to 100 km in the C-band without a dispersion compensator by using vestigial sideband spectrum shaping and nonlinear compensation.

Asynchronous transfer mode link performance over ground networks

NASA Technical Reports Server (NTRS)

Chow, E. T.; Markley, R. W.

1993-01-01

The results of an experiment to determine the feasibility of using asynchronous transfer mode (ATM) technology to support advanced spacecraft missions that require high-rate ground communications and, in particular, full-motion video are reported. Potential nodes in such a ground network include Deep Space Network (DSN) antenna stations, the Jet Propulsion Laboratory, and a set of national and international end users. The experiment simulated a lunar microrover, lunar lander, the DSN ground communications system, and distributed science users. The users were equipped with video-capable workstations. A key feature was an optical fiber link between two high-performance workstations equipped with ATM interfaces. Video was also transmitted through JPL's institutional network to a user 8 km from the experiment. Variations in video depending on the networks and computers were observed, the results are reported.

Cyberbullying Bystanders and Moral Engagement: A Psychosocial Analysis for Pastoral Care

ERIC Educational Resources Information Center

Kyriacou, Chris; Zuin, Antônio

2018-01-01

One of the new challenges facing pastoral care in schools is dealing with the rapid growth of cyberbullying by school-aged children. Within digital cyberspace, cyberbullies are finding more opportunities to express their aggression towards others as social networks become technologically more sophisticated. An important feature of cyberbullying is…

76 FR 56165 - Soliciting Input on Research and Development Priorities for Desirable Features of a Nationwide...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-12

... research and development priorities in anticipation of the President's Wireless Innovation (WIN) Fund to help drive innovation of next-generation network technologies. DATES: Comments are requested by 5 p.m... communities.\\1\\ The Administration has also proposed a $3 billion WIN Fund to help drive innovation through...

Development Communication Report 1988/1-4, Nos. 60-63.

ERIC Educational Resources Information Center

Development Communication Report, 1988

1988-01-01

Four issues of this newsletter focus primarily on the use of communication technologies in developing nations to educate their people. The issues included in this collection are: (1) No. 60 (1988-1), which features articles on the recent emergence of intercountry networks of collaboration (resulting in the sharing of staff, equipment, and…

Multinode data acquisition and control system for the 4-element TACTIC telescope array

NASA Astrophysics Data System (ADS)

Yadav, K. K.; Chouhan, N.; Kaul, S. R.; Koul, R.

2002-03-01

An interrupt driven multinode data acquisition and control system has been developed for the 4-element gamma-ray telescope array, TACTIC. Computer networking technology and the CAMAC bus have been integrated to develop this icon-based, userfriendly failsafe system. The paper describes the salient features of the system.

A systematic approach to infer biological relevance and biases of gene network structures.

PubMed

Antonov, Alexey V; Tetko, Igor V; Mewes, Hans W

2006-01-10

The development of high-throughput technologies has generated the need for bioinformatics approaches to assess the biological relevance of gene networks. Although several tools have been proposed for analysing the enrichment of functional categories in a set of genes, none of them is suitable for evaluating the biological relevance of the gene network. We propose a procedure and develop a web-based resource (BIOREL) to estimate the functional bias (biological relevance) of any given genetic network by integrating different sources of biological information. The weights of the edges in the network may be either binary or continuous. These essential features make our web tool unique among many similar services. BIOREL provides standardized estimations of the network biases extracted from independent data. By the analyses of real data we demonstrate that the potential application of BIOREL ranges from various benchmarking purposes to systematic analysis of the network biology.

Optical Access Networks

NASA Astrophysics Data System (ADS)

Zheng, Jun; Ansari, Nirwan

2005-03-01

Call for Papers: Optical Access Networks With the wide deployment of fiber-optic technology over the past two decades, we have witnessed a tremendous growth of bandwidth capacity in the backbone networks of today's telecommunications infrastructure. However, access networks, which cover the "last-mile" areas and serve numerous residential and small business users, have not been scaled up commensurately. The local subscriber lines for telephone and cable television are still using twisted pairs and coaxial cables. Most residential connections to the Internet are still through dial-up modems operating at a low speed on twisted pairs. As the demand for access bandwidth increases with emerging high-bandwidth applications, such as distance learning, high-definition television (HDTV), and video on demand (VoD), the last-mile access networks have become a bandwidth bottleneck in today's telecommunications infrastructure. To ease this bottleneck, it is imperative to provide sufficient bandwidth capacity in the access networks to open the bottleneck and thus present more opportunities for the provisioning of multiservices. Optical access solutions promise huge bandwidth to service providers and low-cost high-bandwidth services to end users and are therefore widely considered the technology of choice for next-generation access networks. To realize the vision of optical access networks, however, many key issues still need to be addressed, such as network architectures, signaling protocols, and implementation standards. The major challenges lie in the fact that an optical solution must be not only robust, scalable, and flexible, but also implemented at a low cost comparable to that of existing access solutions in order to increase the economic viability of many potential high-bandwidth applications. In recent years, optical access networks have been receiving tremendous attention from both academia and industry. A large number of research activities have been carried out or are now underway this hot area. The purpose of this feature issue is to expose the networking community to the latest research breakthroughs and progresses in the area of optical access networks. This feature issue aims to present a collection of papers that focus on the state-of-the-art research in various networking aspects of optical access networks. Original papers are solicited from all researchers involved in area of optical access networks. Topics of interest include but not limited to:

Functional network in posttranslational modifications: Glyco-Net in Glycoconjugate Data Bank.

PubMed

Miura, Nobuaki; Okada, Takuya; Murayama, Daisuke; Hirose, Kazuko; Sato, Taku; Hashimoto, Ryo; Fukushima, Nobuhiro

2015-01-01

Elucidating pathways related to posttranslational modifications (PTMs) such as glycosylation is of growing importance in post-genome science and technology. Graphical networks describing the relationships among glycan-related molecules, including genes, proteins, lipids, and various biological events, are considered extremely valuable and convenient tools for the systematic investigation of PTMs. Glyco-Net (http://bibi.sci.hokudai.ac.jp/functions/) can dynamically make network figures among various biological molecules and biological events. A certain molecule or event is expressed with a node, and the relationship between the molecule and the event is indicated by arrows in the network figures. In this chapter, we mention the features and current status of the Glyco-Net and a simple example of the search with the Glyco-Net.

A review on equipped hospital beds with wireless sensor networks for reducing bedsores

PubMed Central

Ajami, Sima; Khaleghi, Lida

2015-01-01

At present, the solutions to prevent bedsore include using various techniques for movement and displacement of patients, which is not possible for some patients or dangerous for some of them while it also poses problems for health care providers. On the other hand, development of information technology in the health care system including application of wireless sensor networks (WSNs) has led to easy and quick service-providing. It can provide a solution to prevent bedsore in motionless and disabled patients. Hence, the aim of this article was first to introduce WSNs in hospital beds and second, to identify the benefits and challenges in implementing this technology. This study was a nonsystematic review. The literature was searched for WSNs to reduce and prevent bedsores with the help of libraries, databases (PubMed, SCOPUS, and EMBASE), and also searches engines available at Google Scholar including during 1974-2014 while the inclusion criteria were applied in English and Persian. In our searches, we employed the following keywords and their combinations: “wireless sensor network,” “smart bed,” “information technology,” “smart mattress,” and “bedsore” in the searching areas of titles, keywords, abstracts, and full texts. In this study, more than 45 articles and reports were collected and 37 of them were selected based on their relevance. Therefore, identification and implementation of this technology will be a step toward mechanization of traditional procedures in providing care for hospitalized patients and disabled people. The smart bed and mattress, either alone or in combination with the other technologies, should be capable of providing all of the novel features while still providing the comfort and safety features usually associated with traditional and hospital mattresses. It can eliminate the expense of bedsore in the intensive care unit (ICU) department in the hospital and save much expense there. PMID:26929768

Some new classification methods for hyperspectral remote sensing

NASA Astrophysics Data System (ADS)

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

2006-10-01

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

Imbalance aware lithography hotspot detection: a deep learning approach

NASA Astrophysics Data System (ADS)

Yang, Haoyu; Luo, Luyang; Su, Jing; Lin, Chenxi; Yu, Bei

2017-03-01

With the advancement of VLSI technology nodes, light diffraction caused lithographic hotspots have become a serious problem affecting manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional lithography hotspot detection methods, such as machine learning, have gained satisfactory performance, with extreme scaling of transistor feature size and more and more complicated layout patterns, conventional methodologies may suffer from performance degradation. For example, manual or ad hoc feature extraction in a machine learning framework may lose important information when predicting potential errors in ultra-large-scale integrated circuit masks. In this paper, we present a deep convolutional neural network (CNN) targeting representative feature learning in lithography hotspot detection. We carefully analyze impact and effectiveness of different CNN hyper-parameters, through which a hotspot-detection-oriented neural network model is established. Because hotspot patterns are always minorities in VLSI mask design, the training data set is highly imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classification accuracy may still suffer from high false negative results (missing hotspots), which is fatal in hotspot detection problems. To address the imbalance problem, we further apply minority upsampling and random-mirror flipping before training the network. Experimental results show that our proposed neural network model achieves highly comparable or better performance on the ICCAD 2012 contest benchmark compared to state-of-the-art hotspot detectors based on deep or representative machine leaning.

Automation of lidar-based hydrologic feature extraction workflows using GIS

NASA Astrophysics Data System (ADS)

Borlongan, Noel Jerome B.; de la Cruz, Roel M.; Olfindo, Nestor T.; Perez, Anjillyn Mae C.

2016-10-01

With the advent of LiDAR technology, higher resolution datasets become available for use in different remote sensing and GIS applications. One significant application of LiDAR datasets in the Philippines is in resource features extraction. Feature extraction using LiDAR datasets require complex and repetitive workflows which can take a lot of time for researchers through manual execution and supervision. The Development of the Philippine Hydrologic Dataset for Watersheds from LiDAR Surveys (PHD), a project under the Nationwide Detailed Resources Assessment Using LiDAR (Phil-LiDAR 2) program, created a set of scripts, the PHD Toolkit, to automate its processes and workflows necessary for hydrologic features extraction specifically Streams and Drainages, Irrigation Network, and Inland Wetlands, using LiDAR Datasets. These scripts are created in Python and can be added in the ArcGIS® environment as a toolbox. The toolkit is currently being used as an aid for the researchers in hydrologic feature extraction by simplifying the workflows, eliminating human errors when providing the inputs, and providing quick and easy-to-use tools for repetitive tasks. This paper discusses the actual implementation of different workflows developed by Phil-LiDAR 2 Project 4 in Streams, Irrigation Network and Inland Wetlands extraction.

Feature Extraction for Track Section Status Classification Based on UGW Signals

PubMed Central

Yang, Yuan; Shi, Lin

2018-01-01

Track status classification is essential for the stability and safety of railway operations nowadays, when railway networks are becoming more and more complex and broad. In this situation, monitoring systems are already a key element in applications dedicated to evaluating the status of a certain track section, often determining whether it is free or occupied by a train. Different technologies have already been involved in the design of monitoring systems, including ultrasonic guided waves (UGW). This work proposes the use of the UGW signals captured by a track monitoring system to extract the features that are relevant for determining the corresponding track section status. For that purpose, three features of UGW signals have been considered: the root mean square value, the energy, and the main frequency components. Experimental results successfully validated how these features can be used to classify the track section status into free, occupied and broken. Furthermore, spatial and temporal dependencies among these features were analysed in order to show how they can improve the final classification performance. Finally, a preliminary high-level classification system based on deep learning networks has been envisaged for future works. PMID:29673156

Design and evaluation of a peer network to support adherence to a web-based intervention for adolescents

PubMed Central

Ho, Joyce; Corden, Marya E.; Caccamo, Lauren; Tomasino, Kathryn Noth; Duffecy, Jenna; Begale, Mark; Mohr, David C.

2016-01-01

Background Depression during adolescence is common but can be prevented. Behavioral intervention technologies (BITs) designed to prevent depression in adolescence, especially standalone web-based interventions, have shown mixed outcomes, likely due to poor intervention adherence. BIT research involving adults has shown that the presence of coaches or peers promotes intervention use. Developmentally, adolescence is a time when peer-based social relationships take precedence. This study examines whether peer-networked support may promote adherence to BITs in this age group. Objective Adopting the framework of the Supportive Accountability model, which defines the types of human support and interactions required to maintain engagement and persistence with BITs, this paper presents a feasibility study of a peer-networked online intervention for depression prevention among adolescents. We described the development of the peer network, the evaluation of participant use of the peer networking features, and qualitative user feedback to inform continued BIT development. Method Two groups of adolescents (N = 13) participated in 10-week programs of the peer networked based online intervention. Adolescents had access to didactic lessons, CBT based mood management tools, and peer networking features. The peer networking features are integrated into the site by making use expectations explicit, allow network members to monitor the activities of others, and to supportively hold each other accountable for meeting use expectations. The study collected qualitative feedback from participants as well as usage of site features and tools. Results Participants logged in an average of 12.8 sessions over an average of 10.4 unique days during the 10-week program. On average, 66% of all use sessions occurred within the first 3 weeks of use. The number of “exchange comments”, that is, comments posted that were part of an exchange between two or more participants, was significantly positively correlated with mean time spent on site (r = 0.62, p = 0.032), use of the Activity Tracker (r = 0.70, p = 0.012) and Didactic Lesson (r = 0.73, p = 0.007). Qualitative interviews revealed that adolescents generally liked and were motivated by the peer networking features during the first weeks of the intervention when general site use by group members was high. However, the decrease of site use by group members during the subsequent weeks negatively affected participants’ desire to log on or engage with group members. Conclusions This pilot study highlights the potential that a BIT designed to harness the connection among a peer network, thereby promoting supportive accountability, may improve adolescent adherence to BITs for depression prevention. PMID:27722095

Integrated RF/Optical Interplanetary Networking Preliminary Explorations and Empirical Results

NASA Technical Reports Server (NTRS)

Raible, Daniel E.; Hylton, Alan G.

2012-01-01

Over the last decade interplanetary telecommunication capabilities have been significantly expanded--specifically in support of the Mars exploration rover and lander missions. NASA is continuing to drive advances in new, high payoff optical communications technologies to enhance the network to Gbps performance from Mars, and the transition from technology demonstration to operational system is examined through a hybrid RF/optical approach. Such a system combines the best features of RF and optical communications considering availability and performance to realize a dual band trunk line operating within characteristic constraints. Disconnection due to planetary obscuration and solar conjunction, link delays, timing, ground terminal mission congestion and scheduling policy along with space and atmospheric weather disruptions all imply the need for network protocol solutions to ultimately manage the physical layer in a transparent manner to the end user. Delay Tolerant Networking (DTN) is an approach under evaluation which addresses these challenges. A multi-hop multi-path hybrid RF and optical test bed has been constructed to emulate the integrated deep space network and to support protocol and hardware refinement. Initial experimental results characterize several of these challenges and evaluate the effectiveness of DTN as a solution to mitigate them.

Optical Access Networks

NASA Astrophysics Data System (ADS)

Zheng, Jun; Ansari, Nirwan

2005-06-01

Call for Papers: Optical Access Networks With the wide deployment of fiber-optic technology over the past two decades, we have witnessed a tremendous growth of bandwidth capacity in the backbone networks of today's telecommunications infrastructure. However, access networks, which cover the "last-mile" areas and serve numerous residential and small business users, have not been scaled up commensurately. The local subscriber lines for telephone and cable television are still using twisted pairs and coaxial cables. Most residential connections to the Internet are still through dial-up modems operating at a low speed on twisted pairs. As the demand for access bandwidth increases with emerging high-bandwidth applications, such as distance learning, high-definition television (HDTV), and video on demand (VoD), the last-mile access networks have become a bandwidth bottleneck in today's telecommunications infrastructure. To ease this bottleneck, it is imperative to provide sufficient bandwidth capacity in the access networks to open the bottleneck and thus present more opportunities for the provisioning of multiservices. Optical access solutions promise huge bandwidth to service providers and low-cost high-bandwidth services to end users and are therefore widely considered the technology of choice for next-generation access networks. To realize the vision of optical access networks, however, many key issues still need to be addressed, such as network architectures, signaling protocols, and implementation standards. The major challenges lie in the fact that an optical solution must be not only robust, scalable, and flexible, but also implemented at a low cost comparable to that of existing access solutions in order to increase the economic viability of many potential high-bandwidth applications. In recent years, optical access networks have been receiving tremendous attention from both academia and industry. A large number of research activities have been carried out or are now underway this hot area. The purpose of this feature issue is to expose the networking community to the latest research breakthroughs and progresses in the area of optical access networks. This feature issue aims to present a collection of papers that focus on the state-of-the-art research in various networking aspects of optical access networks. Original papers are solicited from all researchers involved in area of optical access networks. Topics of interest include but not limited to: Optical access network architectures and protocols Passive optical networks (BPON, EPON, GPON, etc.) Active optical networks Multiple access control Multiservices and QoS provisioning Network survivability Field trials and standards Performance modeling and analysis

Optical Access Networks

NASA Astrophysics Data System (ADS)

Zheng, Jun; Ansari, Nirwan; Jersey Inst Ansari, New; Jersey Inst, New

2005-04-01

Call for Papers: Optical Access Networks With the wide deployment of fiber-optic technology over the past two decades, we have witnessed a tremendous growth of bandwidth capacity in the backbone networks of today's telecommunications infrastructure. However, access networks, which cover the "last-mile" areas and serve numerous residential and small business users, have not been scaled up commensurately. The local subscriber lines for telephone and cable television are still using twisted pairs and coaxial cables. Most residential connections to the Internet are still through dial-up modems operating at a low speed on twisted pairs. As the demand for access bandwidth increases with emerging high-bandwidth applications, such as distance learning, high-definition television (HDTV), and video on demand (VoD), the last-mile access networks have become a bandwidth bottleneck in today's telecommunications infrastructure. To ease this bottleneck, it is imperative to provide sufficient bandwidth capacity in the access networks to open the bottleneck and thus present more opportunities for the provisioning of multiservices. Optical access solutions promise huge bandwidth to service providers and low-cost high-bandwidth services to end users and are therefore widely considered the technology of choice for next-generation access networks. To realize the vision of optical access networks, however, many key issues still need to be addressed, such as network architectures, signaling protocols, and implementation standards. The major challenges lie in the fact that an optical solution must be not only robust, scalable, and flexible, but also implemented at a low cost comparable to that of existing access solutions in order to increase the economic viability of many potential high-bandwidth applications. In recent years, optical access networks have been receiving tremendous attention from both academia and industry. A large number of research activities have been carried out or are now underway this hot area. The purpose of this feature issue is to expose the networking community to the latest research breakthroughs and progresses in the area of optical access networks. This feature issue aims to present a collection of papers that focus on the state-of-the-art research in various networking aspects of optical access networks. Original papers are solicited from all researchers involved in area of optical access networks. Topics of interest include but not limited to: Optical access network architectures and protocols Passive optical networks (BPON, EPON, GPON, etc.) Active optical networks Multiple access control Multiservices and QoS provisioning Network survivability Field trials and standards Performance modeling and analysis

Optical Access Networks

NASA Astrophysics Data System (ADS)

Zheng, Jun; Ansari, Nirwan

2005-05-01

Call for Papers: Optical Access Networks With the wide deployment of fiber-optic technology over the past two decades, we have witnessed a tremendous growth of bandwidth capacity in the backbone networks of today's telecommunications infrastructure. However, access networks, which cover the "last-mile" areas and serve numerous residential and small business users, have not been scaled up commensurately. The local subscriber lines for telephone and cable television are still using twisted pairs and coaxial cables. Most residential connections to the Internet are still through dial-up modems operating at a low speed on twisted pairs. As the demand for access bandwidth increases with emerging high-bandwidth applications, such as distance learning, high-definition television (HDTV), and video on demand (VoD), the last-mile access networks have become a bandwidth bottleneck in today's telecommunications infrastructure. To ease this bottleneck, it is imperative to provide sufficient bandwidth capacity in the access networks to open the bottleneck and thus present more opportunities for the provisioning of multiservices. Optical access solutions promise huge bandwidth to service providers and low-cost high-bandwidth services to end users and are therefore widely considered the technology of choice for next-generation access networks. To realize the vision of optical access networks, however, many key issues still need to be addressed, such as network architectures, signaling protocols, and implementation standards. The major challenges lie in the fact that an optical solution must be not only robust, scalable, and flexible, but also implemented at a low cost comparable to that of existing access solutions in order to increase the economic viability of many potential high-bandwidth applications. In recent years, optical access networks have been receiving tremendous attention from both academia and industry. A large number of research activities have been carried out or are now underway this hot area. The purpose of this feature issue is to expose the networking community to the latest research breakthroughs and progresses in the area of optical access networks. This feature issue aims to present a collection of papers that focus on the state-of-the-art research in various networking aspects of optical access networks. Original papers are solicited from all researchers involved in area of optical access networks. Topics of interest include but not limited to: Optical access network architectures and protocols Passive optical networks (BPON, EPON, GPON, etc.) Active optical networks Multiple access control Multiservices and QoS provisioning Network survivability Field trials and standards Performance modeling and analysis

Embedding dynamical networks into distributed models

NASA Astrophysics Data System (ADS)

Innocenti, Giacomo; Paoletti, Paolo

2015-07-01

Large networks of interacting dynamical systems are well-known for the complex behaviours they are able to display, even when each node features a quite simple dynamics. Despite examples of such networks being widespread both in nature and in technological applications, the interplay between the local and the macroscopic behaviour, through the interconnection topology, is still not completely understood. Moreover, traditional analytical methods for dynamical response analysis fail because of the intrinsically large dimension of the phase space of the network which makes the general problem intractable. Therefore, in this paper we develop an approach aiming to condense all the information in a compact description based on partial differential equations. By focusing on propagative phenomena, rigorous conditions under which the original network dynamical properties can be successfully analysed within the proposed framework are derived as well. A network of Fitzhugh-Nagumo systems is finally used to illustrate the effectiveness of the proposed method.

The middleware architecture supports heterogeneous network systems for module-based personal robot system

NASA Astrophysics Data System (ADS)

Choo, Seongho; Li, Vitaly; Choi, Dong Hee; Jung, Gi Deck; Park, Hong Seong; Ryuh, Youngsun

2005-12-01

On developing the personal robot system presently, the internal architecture is every module those occupy separated functions are connected through heterogeneous network system. This module-based architecture supports specialization and division of labor at not only designing but also implementation, as an effect of this architecture, it can reduce developing times and costs for modules. Furthermore, because every module is connected among other modules through network systems, we can get easy integrations and synergy effect to apply advanced mutual functions by co-working some modules. In this architecture, one of the most important technologies is the network middleware that takes charge communications among each modules connected through heterogeneous networks systems. The network middleware acts as the human nerve system inside of personal robot system; it relays, transmits, and translates information appropriately between modules that are similar to human organizations. The network middleware supports various hardware platform, heterogeneous network systems (Ethernet, Wireless LAN, USB, IEEE 1394, CAN, CDMA-SMS, RS-232C). This paper discussed some mechanisms about our network middleware to intercommunication and routing among modules, methods for real-time data communication and fault-tolerant network service. There have designed and implemented a layered network middleware scheme, distributed routing management, network monitoring/notification technology on heterogeneous networks for these goals. The main theme is how to make routing information in our network middleware. Additionally, with this routing information table, we appended some features. Now we are designing, making a new version network middleware (we call 'OO M/W') that can support object-oriented operation, also are updating program sources itself for object-oriented architecture. It is lighter, faster, and can support more operation systems and heterogeneous network systems, but other general purposed middlewares like CORBA, UPnP, etc. can support only one network protocol or operating system.

Convergence

NASA Astrophysics Data System (ADS)

Darcie, Thomas E.; Doverspike, Robert; Zirngibl, Martin; Korotky, Steven K.

2005-09-01

Call for Papers: Convergence The Journal of Optical Networking (JON) invites submissions to a special issue on Convergence. Convergence has become a popular theme in telecommunications, one that has broad implications across all segments of the industry. Continual evolution of technology and applications continues to erase lines between traditionally separate lines of business, with dramatic consequences for vendors, service providers, and consumers. Spectacular advances in all layers of optical networking-leading to abundant, dynamic, cost-effective, and reliable wide-area and local-area connections-have been essential drivers of this evolution. As services and networks continue to evolve towards some notion of convergence, the continued role of optical networks must be explored. One vision of convergence renders all information in a common packet (especially IP) format. This vision is driven by the proliferation of data services. For example, time-division multiplexed (TDM) voice becomes VoIP. Analog cable-television signals become MPEG bits streamed to digital set-top boxes. T1 or OC-N private lines migrate to Ethernet virtual private networks (VPNs). All these packets coexist peacefully within a single packet-routing methodology built on an optical transport layer that combines the flexibility and cost of data networks with telecom-grade reliability. While this vision is appealing in its simplicity and shared widely, specifics of implementation raise many challenges and differences of opinion. For example, many seek to expand the role of Ethernet in these transport networks, while massive efforts are underway to make traditional TDM networks more data friendly within an evolved but backward-compatible SDH/SONET (synchronous digital hierarchy and synchronous optical network) multiplexing hierarchy. From this common underlying theme follow many specific instantiations. Examples include the convergence at the physical, logical, and operational levels of voice and data, video and data, private-line and virtual private-line, fixed and mobile, and local and long-haul services. These trends have many consequences for consumers, vendors, and carriers. Faced with large volumes of low-margin data traffic mixed with traditional voice services, the need for capital conservation and operational efficiency drives carriers away from today's separate overlay networks for each service and towards "converged" platforms. For example, cable operators require transport of multiple services over both hybrid fiber coax (HFC) and DWDM transport technologies. Local carriers seek an economical architecture to deliver integrated services on optically enabled broadband-access networks. Services over wireless-access networks must coexist with those from wired networks. In each case, convergence of networks and services inspires an important set of questions and challenges, driven by the need for low cost, operational efficiency, service performance requirements, and optical transport technology options. This Feature Issue explores the various interpretations and implications of network convergence pertinent to optical networking. How does convergence affect the evolution of optical transport-layer and control approaches? Are the implied directions consistent with research vision for optical networks? Substantial challenges remain. Papers are solicited across the broad spectrum of interests. These include, but are not limited to: Architecture, design and performance of optical wide-area-network (WAN), metro, and access networks Integration strategies for multiservice transport platforms Access methods that bridge traditional and emerging services Network signaling and control methodologies All-optical packet routing and switching techniques To submit to this special issue, follow the normal procedure for submission to JON, indicating "Convergence feature" in the "Comments" field of the online submission form. For all other questions relating to this feature issue, please send an e-mail to jon@osa.org, subject line "Convergence." Additional information can be found on the JON website: http://www.osa-jon.org/submission/ Submission Deadline: 1 October 2005

Status Quo and Prospective of WeChat in Improving Chinese English Learners' Pronunciation

ERIC Educational Resources Information Center

Wang, Kanghui

2017-01-01

With the ubiquitous usage of wireless, portable, and handheld devices gaining popularity in 21st century, the revolutionary mobile technology introduces digital new media to educational settings, which has changed the way of traditional teaching and learning. WeChat is one of the most popular social networking applications in China featured by its…

Can Online Learning Reproduce the Full College Experience? Center for Policy Innovation Discussion Paper. Number 3

ERIC Educational Resources Information Center

McKeown, Karen D.

2012-01-01

With the tuition cost of traditional colleges and universities soaring and education technology advancing, online courses and degree programs are becoming more common. Some critics argue that an online degree cannot provide all the important features of a traditional college education, from extracurricular activities to new professional networks,…

Identification and Classification of OFDM Based Signals Using Preamble Correlation and Cyclostationary Feature Extraction

DTIC Science & Technology

2009-09-01

rapidly advancing technologies of wireless communication networks are providing enormous opportunities. A large number of users in emerging markets ...base element of the 802.16 frame is the physical slot, having the duration 4ps s t f  (2.10) where sf is the sampling frequency. The number of

PubMed Central

Fernandes, Pedro; Gevaert, Kris; Rothacker, Julie; Saiyed, Taslimarif; Detwiler, Michelle

2012-01-01

This roundtable will feature four international speakers who will discuss national and international collaborative initiatives and outreach efforts in which they participate. They will share how these efforts have facilitated access to cutting-edge technology, fostered new generations of scientists, and ultimately advanced the progression of global scientific research. Open discussion will follow the presentations! Centre for Cellular and Molecular Platforms, National Centre for Biological Sciences, India: experiences in implementing a national high-end core facility organization with the goals of improving regional technology access and enhancing the quality of research for scientists in academia, biotechnology companies, and the biopharmaceutical industry.Monash University Technology Platforms and Broader Victorian and Australian Networks: Australian initiatives to build global research capabilities and identify means to internationally benchmark regional capabilities to ensure delivery of world class infrastructure. Within the context of the current Australian strategic framework, funding considerations will be discussed, along with expectations for partner facilities to collaborate and be fully accessible to academia and industry.Instituto Gulbenkian de Ciencia, Portugal and beyond: Multiple roles of networking in science and extending outreach while consolidating community integration. Discussion will include achievement of community building and integration using concepts of sharing, training, resource availability, and the value and empowerment gained using acquired skills. The role of networking and institutional visibility will also be discussed.PRIME-XS: This EU-funded consortium provides an infrastructure of proteomics technologies to the European research community. The core is formed by six access facilities through which the consortium provides access to their technologies. Twelve partners work together to develop new resources to aid the community including the development of bioinformatic tools to analyze large-scale proteomics data and novel technologies to analyze protein interaction networks, post-translational modifications and more sensitive ways to detect protein and peptide biomarkers in complex samples.

Network Analysis of an Emergent Massively Collaborative Creation on Video Sharing Website

NASA Astrophysics Data System (ADS)

Hamasaki, Masahiro; Takeda, Hideaki; Nishimura, Takuichi

The Web technology enables numerous people to collaborate in creation. We designate it as massively collaborative creation via the Web. As an example of massively collaborative creation, we particularly examine video development on Nico Nico Douga, which is a video sharing website that is popular in Japan. We specifically examine videos on Hatsune Miku, a version of a singing synthesizer application software that has inspired not only song creation but also songwriting, illustration, and video editing. As described herein, creators of interact to create new contents through their social network. In this paper, we analyzed the process of developing thousands of videos based on creators' social networks and investigate relationships among creation activity and social networks. The social network reveals interesting features. Creators generate large and sparse social networks including some centralized communities, and such centralized community's members shared special tags. Different categories of creators have different roles in evolving the network, e.g., songwriters gather more links than other categories, implying that they are triggers to network evolution.

Convergence

NASA Astrophysics Data System (ADS)

Darcie, Thomas E.; Doverspike, Robert; Zirngibl, Martin; Korotky, Steven K.

2004-12-01

Convergence has become a popular theme in telecommunications, one that has broad implications across all segments of the industry. Continual evolution of technology and applications continues to erase lines between traditionally separate lines of business, with dramatic consequences for vendors, service providers, and consumers. Spectacular advances in all layers of optical networking-leading to abundant, dynamic, cost-effective, and reliable wide-area and local-area connections-have been essential drivers of this evolution. As services and networks continue to evolve towards some notion of convergence, the continued role of optical networks must be explored. One vision of convergence renders all information in a common packet (especially IP) format. This vision is driven by the proliferation of data services. For example, time-division multiplexed (TDM) voice becomes VoIP. Analog cable-television signals become MPEG bits streamed to digital set-top boxes. T1 or OC-N private lines migrate to Ethernet virtual private networks (VPNs). All these packets coexist peacefully within a single packet-routing methodology built on an optical transport layer that combines the flexibility and cost of data networks with telecom-grade reliability. While this vision is appealing in its simplicity and shared widely, specifics of implementation raise many challenges and differences of opinion. For example, many seek to expand the role of Ethernet in these transport networks, while massive efforts are underway to make traditional TDM networks more data friendly within an evolved but backward-compatible SDH/SONET (synchronous digital hierarchy and synchronous optical network) multiplexing hierarchy. From this common underlying theme follow many specific instantiations. Examples include the convergence at the physical, logical, and operational levels of voice and data, video and data, private-line and virtual private-line, fixed and mobile, and local and long-haul services. These trends have many consequences for consumers, vendors, and carriers. Faced with large volumes of low-margin data traffic mixed with traditional voice services, the need for capital conservation and operational efficiency drives carriers away from today's separate overlay networks for each service and towards "converged" platforms. For example, cable operators require transport of multiple services over both hybrid fiber coax (HFC) and DWDM transport technologies. Local carriers seek an economical architecture to deliver integrated services on optically enabled broadband-access networks. Services over wireless-access networks must coexist with those from wired networks. In each case, convergence of networks and services inspires an important set of questions and challenges, driven by the need for low cost, operational efficiency, service performance requirements, and optical transport technology options. This Feature Issue explores the various interpretations and implications of network convergence pertinent to optical networking. How does convergence affect the evolution of optical transport-layer and control approaches? Are the implied directions consistent with research vision for optical networks? Substantial challenges remain. Papers are solicited across the broad spectrum of interests. These include, but are not limited to:

• Architecture, design and performance of optical wide-area-network (WAN), metro, and access networks
• Integration strategies for multiservice transport platforms
• Access methods that bridge traditional and emerging services
• Network signaling and control methodologies
• All-optical packet routing and switching techniques

Manuscript Submission

A Clustering Graph Generator

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

Winlaw, Manda; De Sterck, Hans; Sanders, Geoffrey

In very simple terms a network can be de ned as a collection of points joined together by lines. Thus, networks can be used to represent connections between entities in a wide variety of elds including engi- neering, science, medicine, and sociology. Many large real-world networks share a surprising number of properties, leading to a strong interest in model development research and techniques for building synthetic networks have been developed, that capture these similarities and replicate real-world graphs. Modeling these real-world networks serves two purposes. First, building models that mimic the patterns and prop- erties of real networks helps tomore » understand the implications of these patterns and helps determine which patterns are important. If we develop a generative process to synthesize real networks we can also examine which growth processes are plausible and which are not. Secondly, high-quality, large-scale network data is often not available, because of economic, legal, technological, or other obstacles [7]. Thus, there are many instances where the systems of interest cannot be represented by a single exemplar network. As one example, consider the eld of cybersecurity, where systems require testing across diverse threat scenarios and validation across diverse network structures. In these cases, where there is no single exemplar network, the systems must instead be modeled as a collection of networks in which the variation among them may be just as important as their common features. By developing processes to build synthetic models, so-called graph generators, we can build synthetic networks that capture both the essential features of a system and realistic variability. Then we can use such synthetic graphs to perform tasks such as simulations, analysis, and decision making. We can also use synthetic graphs to performance test graph analysis algorithms, including clustering algorithms and anomaly detection algorithms.« less

Synchronization in complex networks

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

Arenas, A.; Diaz-Guilera, A.; Moreno, Y.

Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analyticalmore » approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.« less

Three-dimensional fingerprint recognition by using convolution neural network

NASA Astrophysics Data System (ADS)

Tian, Qianyu; Gao, Nan; Zhang, Zonghua

2018-01-01

With the development of science and technology and the improvement of social information, fingerprint recognition technology has become a hot research direction and been widely applied in many actual fields because of its feasibility and reliability. The traditional two-dimensional (2D) fingerprint recognition method relies on matching feature points. This method is not only time-consuming, but also lost three-dimensional (3D) information of fingerprint, with the fingerprint rotation, scaling, damage and other issues, a serious decline in robustness. To solve these problems, 3D fingerprint has been used to recognize human being. Because it is a new research field, there are still lots of challenging problems in 3D fingerprint recognition. This paper presents a new 3D fingerprint recognition method by using a convolution neural network (CNN). By combining 2D fingerprint and fingerprint depth map into CNN, and then through another CNN feature fusion, the characteristics of the fusion complete 3D fingerprint recognition after classification. This method not only can preserve 3D information of fingerprints, but also solves the problem of CNN input. Moreover, the recognition process is simpler than traditional feature point matching algorithm. 3D fingerprint recognition rate by using CNN is compared with other fingerprint recognition algorithms. The experimental results show that the proposed 3D fingerprint recognition method has good recognition rate and robustness.

QuakeSim Project Networking

NASA Astrophysics Data System (ADS)

Kong, D.; Donnellan, A.; Pierce, M. E.

2012-12-01

QuakeSim is an online computational framework focused on using remotely sensed geodetic imaging data to model and understand earthquakes. With the rise in online social networking over the last decade, many tools and concepts have been developed that are useful to research groups. In particular, QuakeSim is interested in the ability for researchers to post, share, and annotate files generated by modeling tools in order to facilitate collaboration. To accomplish this, features were added to the preexisting QuakeSim site that include single sign-on, automated saving of output from modeling tools, and a personal user space to manage sharing permissions on these saved files. These features implement OpenID and Lightweight Data Access Protocol (LDAP) technologies to manage files across several different servers, including a web server running Drupal and other servers hosting the computational tools themselves.

Research on marine and freshwater fish identification model based on hyper-spectral imaging technology

NASA Astrophysics Data System (ADS)

Fu, Yan; Guo, Pei-yuan; Xiang, Ling-zi; Bao, Man; Chen, Xing-hai

2013-08-01

With the gradually mature of hyper spectral image technology, the application of the meat nondestructive detection and recognition has become one of the current research focuses. This paper for the study of marine and freshwater fish by the pre-processing and feature extraction of the collected spectral curve data, combined with BP network structure and LVQ network structure, a predictive model of hyper spectral image data of marine and freshwater fish has been initially established and finally realized the qualitative analysis and identification of marine and freshwater fish quality. The results of this study show that hyper spectral imaging technology combined with the BP and LVQ Artificial Neural Network Model can be used for the identification of marine and freshwater fish detection. Hyper-spectral data acquisition can be carried out without any pretreatment of the samples, thus hyper-spectral imaging technique is the lossless, high- accuracy and rapid detection method for quality of fish. In this study, only 30 samples are used for the exploratory qualitative identification of research, although the ideal study results are achieved, we will further increase the sample capacity to take the analysis of quantitative identification and verify the feasibility of this theory.

Ship electric propulsion simulator based on networking technology

NASA Astrophysics Data System (ADS)

Zheng, Huayao; Huang, Xuewu; Chen, Jutao; Lu, Binquan

2006-11-01

According the new ship building tense, a novel electric propulsion simulator (EPS) had been developed in Marine Simulation Center of SMU. The architecture, software function and FCS network technology of EPS and integrated power system (IPS) were described. In allusion to the POD propeller in ship, a special physical model was built. The POD power was supplied from the simulative 6.6 kV Medium Voltage Main Switchboard, its control could be realized in local or remote mode. Through LAN, the simulated feature information of EPS will pass to the physical POD model, which would reflect the real thruster working status in different sea conditions. The software includes vessel-propeller math module, thruster control system, distribution and emergency integrated management, double closed loop control system, vessel static water resistance and dynamic software; instructor main control software. The monitor and control system is realized by real time data collection system and CAN bus technology. During the construction, most devices such as monitor panels and intelligent meters, are developed in lab which were based on embedded microcomputer system with CAN interface to link the network. They had also successfully used in practice and would be suitable for the future demands of digitalization ship.

Applying Gradient Descent in Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Cui, Nan

2018-04-01

With the development of the integrated circuit and computer science, people become caring more about solving practical issues via information technologies. Along with that, a new subject called Artificial Intelligent (AI) comes up. One popular research interest of AI is about recognition algorithm. In this paper, one of the most common algorithms, Convolutional Neural Networks (CNNs) will be introduced, for image recognition. Understanding its theory and structure is of great significance for every scholar who is interested in this field. Convolution Neural Network is an artificial neural network which combines the mathematical method of convolution and neural network. The hieratical structure of CNN provides it reliable computer speed and reasonable error rate. The most significant characteristics of CNNs are feature extraction, weight sharing and dimension reduction. Meanwhile, combining with the Back Propagation (BP) mechanism and the Gradient Descent (GD) method, CNNs has the ability to self-study and in-depth learning. Basically, BP provides an opportunity for backwardfeedback for enhancing reliability and GD is used for self-training process. This paper mainly discusses the CNN and the related BP and GD algorithms, including the basic structure and function of CNN, details of each layer, the principles and features of BP and GD, and some examples in practice with a summary in the end.

Trusted Computing Technologies, Intel Trusted Execution Technology.

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

Guise, Max Joseph; Wendt, Jeremy Daniel

2011-01-01

We describe the current state-of-the-art in Trusted Computing Technologies - focusing mainly on Intel's Trusted Execution Technology (TXT). This document is based on existing documentation and tests of two existing TXT-based systems: Intel's Trusted Boot and Invisible Things Lab's Qubes OS. We describe what features are lacking in current implementations, describe what a mature system could provide, and present a list of developments to watch. Critical systems perform operation-critical computations on high importance data. In such systems, the inputs, computation steps, and outputs may be highly sensitive. Sensitive components must be protected from both unauthorized release, and unauthorized alteration: Unauthorizedmore » users should not access the sensitive input and sensitive output data, nor be able to alter them; the computation contains intermediate data with the same requirements, and executes algorithms that the unauthorized should not be able to know or alter. Due to various system requirements, such critical systems are frequently built from commercial hardware, employ commercial software, and require network access. These hardware, software, and network system components increase the risk that sensitive input data, computation, and output data may be compromised.« less

The Role of Social Network Technologies in Online Health Promotion: A Narrative Review of Theoretical and Empirical Factors Influencing Intervention Effectiveness.

PubMed

Balatsoukas, Panos; Kennedy, Catriona M; Buchan, Iain; Powell, John; Ainsworth, John

2015-06-11

Social network technologies have become part of health education and wider health promotion—either by design or happenstance. Social support, peer pressure, and information sharing in online communities may affect health behaviors. If there are positive and sustained effects, then social network technologies could increase the effectiveness and efficiency of many public health campaigns. Social media alone, however, may be insufficient to promote health. Furthermore, there may be unintended and potentially harmful consequences of inaccurate or misleading health information. Given these uncertainties, there is a need to understand and synthesize the evidence base for the use of online social networking as part of health promoting interventions to inform future research and practice. Our aim was to review the research on the integration of expert-led health promotion interventions with online social networking in order to determine the extent to which the complementary benefits of each are understood and used. We asked, in particular, (1) How is effectiveness being measured and what are the specific problems in effecting health behavior change?, and (2) To what extent is the designated role of social networking grounded in theory? The narrative synthesis approach to literature review was used to analyze the existing evidence. We searched the indexed scientific literature using keywords associated with health promotion and social networking. The papers included were only those making substantial study of both social networking and health promotion—either reporting the results of the intervention or detailing evidence-based plans. General papers about social networking and health were not included. The search identified 162 potentially relevant documents after review of titles and abstracts. Of these, 42 satisfied the inclusion criteria after full-text review. Six studies described randomized controlled trials (RCTs) evaluating the effectiveness of online social networking within health promotion interventions. Most of the trials investigated the value of a "social networking condition" in general and did not identify specific features that might play a role in effectiveness. Issues about the usability and level of uptake of interventions were more common among pilot studies, while observational studies showed positive evidence about the role of social support. A total of 20 papers showed the use of theory in the design of interventions, but authors evaluated effectiveness in only 10 papers. More research is needed in this area to understand the actual effect of social network technologies on health promotion. More RCTs of greater length need to be conducted taking into account contextual factors such as patient characteristics and types of a social network technology. Also, more evidence is needed regarding the actual usability of online social networking and how different interface design elements may help or hinder behavior change and engagement. Moreover, it is crucial to investigate further the effect of theory on the effectiveness of this type of technology for health promotion. Research is needed linking theoretical grounding with observation and analysis of health promotion in online networks.

Analysis of multiuser mixed RF/FSO relay networks for performance improvements in Cloud Computing-Based Radio Access Networks (CC-RANs)

NASA Astrophysics Data System (ADS)

Alimi, Isiaka A.; Monteiro, Paulo P.; Teixeira, António L.

2017-11-01

The key paths toward the fifth generation (5G) network requirements are towards centralized processing and small-cell densification systems that are implemented on the cloud computing-based radio access networks (CC-RANs). The increasing recognitions of the CC-RANs can be attributed to their valuable features regarding system performance optimization and cost-effectiveness. Nevertheless, realization of the stringent requirements of the fronthaul that connects the network elements is highly demanding. In this paper, considering the small-cell network architectures, we present multiuser mixed radio-frequency/free-space optical (RF/FSO) relay networks as feasible technologies for the alleviation of the stringent requirements in the CC-RANs. In this study, we use the end-to-end (e2e) outage probability, average symbol error probability (ASEP), and ergodic channel capacity as the performance metrics in our analysis. Simulation results show the suitability of deployment of mixed RF/FSO schemes in the real-life scenarios.

Artificial odor discrimination system using electronic nose and neural networks for the identification of urinary tract infection.

PubMed

Kodogiannis, Vassilis S; Lygouras, John N; Tarczynski, Andrzej; Chowdrey, Hardial S

2008-11-01

Current clinical diagnostics are based on biochemical, immunological, or microbiological methods. However, these methods are operator dependent, time-consuming, expensive, and require special skills, and are therefore, not suitable for point-of-care testing. Recent developments in gas-sensing technology and pattern recognition methods make electronic nose technology an interesting alternative for medical point-of-care devices. An electronic nose has been used to detect urinary tract infection from 45 suspected cases that were sent for analysis in a U.K. Public Health Registry. These samples were analyzed by incubation in a volatile generation test tube system for 4-5 h. Two issues are being addressed, including the implementation of an advanced neural network, based on a modified expectation maximization scheme that incorporates a dynamic structure methodology and the concept of a fusion of multiple classifiers dedicated to specific feature parameters. This study has shown the potential for early detection of microbial contaminants in urine samples using electronic nose technology.

Deep Space Network information system architecture study

NASA Technical Reports Server (NTRS)

Beswick, C. A.; Markley, R. W. (Editor); Atkinson, D. J.; Cooper, L. P.; Tausworthe, R. C.; Masline, R. C.; Jenkins, J. S.; Crowe, R. A.; Thomas, J. L.; Stoloff, M. J.

1992-01-01

The purpose of this article is to describe an architecture for the DSN information system in the years 2000-2010 and to provide guidelines for its evolution during the 1990's. The study scope is defined to be from the front-end areas at the antennas to the end users (spacecraft teams, principal investigators, archival storage systems, and non-NASA partners). The architectural vision provides guidance for major DSN implementation efforts during the next decade. A strong motivation for the study is an expected dramatic improvement in information-systems technologies--i.e., computer processing, automation technology (including knowledge-based systems), networking and data transport, software and hardware engineering, and human-interface technology. The proposed Ground Information System has the following major features: unified architecture from the front-end area to the end user; open-systems standards to achieve interoperability; DSN production of level 0 data; delivery of level 0 data from the Deep Space Communications Complex, if desired; dedicated telemetry processors for each receiver; security against unauthorized access and errors; and highly automated monitor and control.

Imbalance aware lithography hotspot detection: a deep learning approach

NASA Astrophysics Data System (ADS)

Yang, Haoyu; Luo, Luyang; Su, Jing; Lin, Chenxi; Yu, Bei

2017-07-01

With the advancement of very large scale integrated circuits (VLSI) technology nodes, lithographic hotspots become a serious problem that affects manufacture yield. Lithography hotspot detection at the post-OPC stage is imperative to check potential circuit failures when transferring designed patterns onto silicon wafers. Although conventional lithography hotspot detection methods, such as machine learning, have gained satisfactory performance, with the extreme scaling of transistor feature size and layout patterns growing in complexity, conventional methodologies may suffer from performance degradation. For example, manual or ad hoc feature extraction in a machine learning framework may lose important information when predicting potential errors in ultra-large-scale integrated circuit masks. We present a deep convolutional neural network (CNN) that targets representative feature learning in lithography hotspot detection. We carefully analyze the impact and effectiveness of different CNN hyperparameters, through which a hotspot-detection-oriented neural network model is established. Because hotspot patterns are always in the minority in VLSI mask design, the training dataset is highly imbalanced. In this situation, a neural network is no longer reliable, because a trained model with high classification accuracy may still suffer from a high number of false negative results (missing hotspots), which is fatal in hotspot detection problems. To address the imbalance problem, we further apply hotspot upsampling and random-mirror flipping before training the network. Experimental results show that our proposed neural network model achieves comparable or better performance on the ICCAD 2012 contest benchmark compared to state-of-the-art hotspot detectors based on deep or representative machine leaning.

Visible rodent brain-wide networks at single-neuron resolution

PubMed Central

Yuan, Jing; Gong, Hui; Li, Anan; Li, Xiangning; Chen, Shangbin; Zeng, Shaoqun; Luo, Qingming

2015-01-01

There are some unsolvable fundamental questions, such as cell type classification, neural circuit tracing and neurovascular coupling, though great progresses are being made in neuroscience. Because of the structural features of neurons and neural circuits, the solution of these questions needs us to break through the current technology of neuroanatomy for acquiring the exactly fine morphology of neuron and vessels and tracing long-distant circuit at axonal resolution in the whole brain of mammals. Combined with fast-developing labeling techniques, efficient whole-brain optical imaging technology emerging at the right moment presents a huge potential in the structure and function research of specific-function neuron and neural circuit. In this review, we summarize brain-wide optical tomography techniques, review the progress on visible brain neuronal/vascular networks benefit from these novel techniques, and prospect the future technical development. PMID:26074784

Securing the Global Airspace System Via Identity-Based Security

NASA Technical Reports Server (NTRS)

Ivancic, William D.

2015-01-01

Current telecommunications systems have very good security architectures that include authentication and authorization as well as accounting. These three features enable an edge system to obtain access into a radio communication network, request specific Quality-of-Service (QoS) requirements and ensure proper billing for service. Furthermore, the links are secure. Widely used telecommunication technologies are Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX) This paper provides a system-level view of network-centric operations for the global airspace system and the problems and issues with deploying new technologies into the system. The paper then focuses on applying the basic security architectures of commercial telecommunication systems and deployment of federated Authentication, Authorization and Accounting systems to provide a scalable, evolvable reliable and maintainable solution to enable a globally deployable identity-based secure airspace system.

Artificial intelligence for analyzing orthopedic trauma radiographs.

PubMed

Olczak, Jakub; Fahlberg, Niklas; Maki, Atsuto; Razavian, Ali Sharif; Jilert, Anthony; Stark, André; Sköldenberg, Olof; Gordon, Max

2017-12-01

Background and purpose - Recent advances in artificial intelligence (deep learning) have shown remarkable performance in classifying non-medical images, and the technology is believed to be the next technological revolution. So far it has never been applied in an orthopedic setting, and in this study we sought to determine the feasibility of using deep learning for skeletal radiographs. Methods - We extracted 256,000 wrist, hand, and ankle radiographs from Danderyd's Hospital and identified 4 classes: fracture, laterality, body part, and exam view. We then selected 5 openly available deep learning networks that were adapted for these images. The most accurate network was benchmarked against a gold standard for fractures. We furthermore compared the network's performance with 2 senior orthopedic surgeons who reviewed images at the same resolution as the network. Results - All networks exhibited an accuracy of at least 90% when identifying laterality, body part, and exam view. The final accuracy for fractures was estimated at 83% for the best performing network. The network performed similarly to senior orthopedic surgeons when presented with images at the same resolution as the network. The 2 reviewer Cohen's kappa under these conditions was 0.76. Interpretation - This study supports the use for orthopedic radiographs of artificial intelligence, which can perform at a human level. While current implementation lacks important features that surgeons require, e.g. risk of dislocation, classifications, measurements, and combining multiple exam views, these problems have technical solutions that are waiting to be implemented for orthopedics.

A Secure and Efficient Handover Authentication Protocol for Wireless Networks

PubMed Central

Wang, Weijia; Hu, Lei

2014-01-01

Handover authentication protocol is a promising access control technology in the fields of WLANs and mobile wireless sensor networks. In this paper, we firstly review an efficient handover authentication protocol, named PairHand, and its existing security attacks and improvements. Then, we present an improved key recovery attack by using the linearly combining method and reanalyze its feasibility on the improved PairHand protocol. Finally, we present a new handover authentication protocol, which not only achieves the same desirable efficiency features of PairHand, but enjoys the provable security in the random oracle model. PMID:24971471

How behavioral science can advance digital health.

PubMed

Pagoto, Sherry; Bennett, Gary G

2013-09-01

The field of behavioral science has produced myriad data on health behavior change strategies and leveraged such data into effective human-delivered interventions to improve health. Unfortunately, the impact of traditional health behavior change interventions has been heavily constrained by patient and provider burden, limited ability to measure and intervene upon behavior in real time, variable adherence, low rates of implementation, and poor third-party coverage. Digital health technologies, including mobile phones, sensors, and online social networks, by being available in real time, are being explored as tools to increase our understanding of health behavior and to enhance the impact of behavioral interventions. The recent explosion of industry attention to the development of novel health technologies is exciting but has far outpaced research. This Special Section of Translational Behavioral Medicine, Smartphones, Sensors, and Social Networks: A New Age of Health Behavior Change features a collection of studies that leverage health technologies to measure, change, and/or understand health behavior. We propose five key areas in which behavioral science can improve the impact of digital health technologies on public health. First, research is needed to identify which health technologies actually impact behavior and health outcomes. Second, we need to understand how online social networks can be leveraged to impact health behavior on a large scale. Third, a team science approach is needed in the developmental process of health technologies. Fourth, behavioral scientists should identify how a balance can be struck between the fast pace of innovation and the much slower pace of research. Fifth, behavioral scientists have an integral role in informing the development of health technologies and facilitating the movement of health technologies into the healthcare system.

Spinoff 2001: Special Millennium Feature

NASA Technical Reports Server (NTRS)

2001-01-01

For the past 43 years, NASA has devoted its facilities, labor force, and expertise to sharing the abundance of technology developments used for its missions with the nation's industries. These countless technologies have not only successfully contributed to the growth of the U.S. economy, but also to the quality of life on Earth. For the past 25 years, NASA's Spinoff publication has brought attention to thousands of technologies, products, and services that were developed as a direct result of commercial partnerships between NASA and the private business sector. Many of these exciting technologies included advances in ceramics, computer technology, fiber optics, and remote sensing. New and ongoing research at the NASA field centers covers a full spectrum of technologies that will provide numerous advantages for the future, many of which have made significant strides in the commercial market. The NASA Commercial Technology Network plays a large role in transferring this progress. By applying NASA technologies such as data communication, aircraft de-icing technologies, and innovative materials to everyday functions, American consumers and the national economy benefit. Moving forward into the new millennium, these new technologies will further advance our country's position as the world leader in scientific and technical innovation. These cutting-edge innovations represent the investment of the U.S. citizen in the Space Program. Some of these technologies are highlighted in Spinoff 2001, an example of NASA's commitment to technology transfer and commercialization assistance. This year's issue spotlights the commercial technology efforts of NASA's John F. Kennedy Space Center. Kennedy's extensive network of commercial technology opportunities has enabled them to become a leader in technology transfer outreach. This kind of leadership is exemplified through Kennedy's recent partnership with the State of Florida, working toward the development of the Space Experiment Research and Processing Laboratory. The new laboratory is the first step toward the development of a proposed 400-acre Space Commerce Park, located at Kennedy Space Center. Spinoff, once again, successfully showcases the variety of commercial successes and benefits resulting from the transfer of NASA technology to private industry. It is with great pride and pleasure that we present Spinoff 2001 with a Special Millennium Feature. With help from U.S. industry and commercial technology programs, NASA will continue to assist in the presentation of innovative new products to our nation.

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

Automated information-analytical system for thunderstorm monitoring and early warning alarms using modern physical sensors and information technologies with elements of artificial intelligence

NASA Astrophysics Data System (ADS)

Boldyreff, Anton S.; Bespalov, Dmitry A.; Adzhiev, Anatoly Kh.

2017-05-01

Methods of artificial intelligence are a good solution for weather phenomena forecasting. They allow to process a large amount of diverse data. Recirculation Neural Networks is implemented in the paper for the system of thunderstorm events prediction. Large amounts of experimental data from lightning sensors and electric field mills networks are received and analyzed. The average recognition accuracy of sensor signals is calculated. It is shown that Recirculation Neural Networks is a promising solution in the forecasting of thunderstorms and weather phenomena, characterized by the high efficiency of the recognition elements of the sensor signals, allows to compress images and highlight their characteristic features for subsequent recognition.

Fully convolutional network with cluster for semantic segmentation

NASA Astrophysics Data System (ADS)

Ma, Xiao; Chen, Zhongbi; Zhang, Jianlin

2018-04-01

At present, image semantic segmentation technology has been an active research topic for scientists in the field of computer vision and artificial intelligence. Especially, the extensive research of deep neural network in image recognition greatly promotes the development of semantic segmentation. This paper puts forward a method based on fully convolutional network, by cluster algorithm k-means. The cluster algorithm using the image's low-level features and initializing the cluster centers by the super-pixel segmentation is proposed to correct the set of points with low reliability, which are mistakenly classified in great probability, by the set of points with high reliability in each clustering regions. This method refines the segmentation of the target contour and improves the accuracy of the image segmentation.

Worm epidemics in wireless ad hoc networks

NASA Astrophysics Data System (ADS)

Nekovee, Maziar

2007-06-01

A dramatic increase in the number of computing devices with wireless communication capability has resulted in the emergence of a new class of computer worms which specifically target such devices. The most striking feature of these worms is that they do not require Internet connectivity for their propagation but can spread directly from device to device using a short-range radio communication technology, such as WiFi or Bluetooth. In this paper, we develop a new model for epidemic spreading of these worms and investigate their spreading in wireless ad hoc networks via extensive Monte Carlo simulations. Our studies show that the threshold behaviour and dynamics of worm epidemics in these networks are greatly affected by a combination of spatial and temporal correlations which characterize these networks, and are significantly different from the previously studied epidemics in the Internet.

Direct laser written polymer waveguides with out of plane couplers for optical chips

NASA Astrophysics Data System (ADS)

Landowski, Alexander; Zepp, Dominik; Wingerter, Sebastian; von Freymann, Georg; Widera, Artur

2017-10-01

Optical technologies call for waveguide networks featuring high integration densities, low losses, and simple operation. Here, we present polymer waveguides fabricated from a negative tone photoresist via two-photon-lithography in direct laser writing, and show a detailed parameter study of their performance. Specifically, we produce waveguides featuring bend radii down to 40 μ m, insertion losses of the order of 10 dB, and loss coefficients smaller than 0.81 dB mm-1, facilitating high integration densities in writing fields of 300 μ m×300 μ m. A novel three-dimensional coupler design allows for coupling control as well as direct observation of outputs in a single field of view through a microscope objective. Finally, we present beam-splitting devices to construct larger optical networks, and we show that the waveguide material is compatible with the integration of quantum emitters.

NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres

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

Ortiz-Rodriguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.

In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called ''Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres'', (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the ''Robust design of artificial neural networks methodology'' and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored atmore » synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of {sup 252}Cf, {sup 241}AmBe and {sup 239}PuBe neutron sources measured with a Bonner spheres system.« less

NSDann2BS, a neutron spectrum unfolding code based on neural networks technology and two bonner spheres

NASA Astrophysics Data System (ADS)

Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Solís Sánches, L. O.; Miranda, R. Castañeda; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

2013-07-01

In this work a neutron spectrum unfolding code, based on artificial intelligence technology is presented. The code called "Neutron Spectrometry and Dosimetry with Artificial Neural Networks and two Bonner spheres", (NSDann2BS), was designed in a graphical user interface under the LabVIEW programming environment. The main features of this code are to use an embedded artificial neural network architecture optimized with the "Robust design of artificial neural networks methodology" and to use two Bonner spheres as the only piece of information. In order to build the code here presented, once the net topology was optimized and properly trained, knowledge stored at synaptic weights was extracted and using a graphical framework build on the LabVIEW programming environment, the NSDann2BS code was designed. This code is friendly, intuitive and easy to use for the end user. The code is freely available upon request to authors. To demonstrate the use of the neural net embedded in the NSDann2BS code, the rate counts of 252Cf, 241AmBe and 239PuBe neutron sources measured with a Bonner spheres system.

Uncovering Innovation Features and Emerging Technologies in Molecular Biology through Patent Analysis.

PubMed

Pereira, Cristiano Gonçalves; Porto, Geciane Silveira

2018-01-01

Scientific research at universities has a crucial role in leveraging a country's innovative potential. Sectors that require greater investments in technology for the development of their research, such as biotechnology, need to be aware of the frontier state-of-the-art technology and the knowledge incrusted within it. Although the information available in scientific articles is well explored in academic environment, the patent literature, where much of the technological information is present, is still poorly accessed. This chapter is intended to instruct students and researchers at universities to look at patent document analysis as a source of scientific and technological information and explore its applications. Within this chapter, we use the technological area regarding immunoglobulins inventions (monoclonal and polyclonal antibodies) as example to provide directions on how to develop a patent landscape to get an overview of the inventions in a certain field; how to map a collaborative network of inventors/assignees to help the pursuit and identification of future partnerships; and lastly we describe the steps of how to set up a network of patent citations with the aim of forecasting emerging technologies. We strongly believe that incorporate data from patents in planning phase of research projects at academia, as well as to establish partnerships and join R&D efforts to invest on promising technologies, is of great relevance to leverage the growth of the biotechnology sector.

Competition in the domain of wireless networks security

NASA Astrophysics Data System (ADS)

Bednarczyk, Mariusz

2017-04-01

Wireless networks are very popular and have found wide spread usage amongst various segments, also in military environment. The deployment of wireless infrastructures allow to reduce the time it takes to install and dismantle communications networks. With wireless, users are more mobile and can easily get access to the network resources all the time. However, wireless technologies like WiFi or Bluetooth have security issues that hackers have extensively exploited over the years. In the paper several serious security flaws in wireless technologies are presented. Most of them enable to get access to the internal networks and easily carry out man-in-the-middle attacks. Very often, they are used to launch massive denial of service attacks that target the physical infrastructure as well as the RF spectrum. For instance, there are well known instances of Bluetooth connection spoofing in order to steal WiFi password stored in the mobile device. To raise the security awareness and protect wireless networks against an adversary attack, an analysis of attack methods and tools over time is presented in the article. The particular attention is paid to the severity, possible targets as well as the ability to persist in the context of protective measures. Results show that an adversary can take complete control of the victims' mobile device features if the users forget to use simple safety principles.

Realistic computer network simulation for network intrusion detection dataset generation

NASA Astrophysics Data System (ADS)

Payer, Garrett

2015-05-01

The KDD-99 Cup dataset is dead. While it can continue to be used as a toy example, the age of this dataset makes it all but useless for intrusion detection research and data mining. Many of the attacks used within the dataset are obsolete and do not reflect the features important for intrusion detection in today's networks. Creating a new dataset encompassing a large cross section of the attacks found on the Internet today could be useful, but would eventually fall to the same problem as the KDD-99 Cup; its usefulness would diminish after a period of time. To continue research into intrusion detection, the generation of new datasets needs to be as dynamic and as quick as the attacker. Simply examining existing network traffic and using domain experts such as intrusion analysts to label traffic is inefficient, expensive, and not scalable. The only viable methodology is simulation using technologies including virtualization, attack-toolsets such as Metasploit and Armitage, and sophisticated emulation of threat and user behavior. Simulating actual user behavior and network intrusion events dynamically not only allows researchers to vary scenarios quickly, but enables online testing of intrusion detection mechanisms by interacting with data as it is generated. As new threat behaviors are identified, they can be added to the simulation to make quicker determinations as to the effectiveness of existing and ongoing network intrusion technology, methodology and models.

Upgrading of data acquisition software for centralized radiation monitoring system in Malaysian Nuclear Agency

NASA Astrophysics Data System (ADS)

Yussup, F.; Ibrahim, M. M.; Haris, M. F.; Soh, S. C.; Hasim, H.; Azman, A.; Razalim, F. A. A.; Yapp, R.; Ramli, A. A. M.

2016-01-01

With the growth of technology, many devices and equipments can be connected to the network and internet to enable online data acquisition for real-time data monitoring and control from monitoring devices located at remote sites. Centralized radiation monitoring system (CRMS) is a system that enables area radiation level at various locations in Malaysian Nuclear Agency (Nuklear Malaysia) to be monitored centrally by using a web browser. The Local Area Network (LAN) in Nuclear Malaysia is utilized in CRMS as a communication media for data acquisition of the area radiation levels from radiation detectors. The development of the system involves device configuration, wiring, network and hardware installation, software and web development. This paper describes the software upgrading on the system server that is responsible to acquire and record the area radiation readings from the detectors. The recorded readings are called in a web programming to be displayed on a website. Besides the main feature which is acquiring the area radiation levels in Nuclear Malaysia centrally, the upgrading involves new features such as uniform time interval for data recording and exporting, warning system and dose triggering.

Entropy of network ensembles

NASA Astrophysics Data System (ADS)

Bianconi, Ginestra

2009-03-01

In this paper we generalize the concept of random networks to describe network ensembles with nontrivial features by a statistical mechanics approach. This framework is able to describe undirected and directed network ensembles as well as weighted network ensembles. These networks might have nontrivial community structure or, in the case of networks embedded in a given space, they might have a link probability with a nontrivial dependence on the distance between the nodes. These ensembles are characterized by their entropy, which evaluates the cardinality of networks in the ensemble. In particular, in this paper we define and evaluate the structural entropy, i.e., the entropy of the ensembles of undirected uncorrelated simple networks with given degree sequence. We stress the apparent paradox that scale-free degree distributions are characterized by having small structural entropy while they are so widely encountered in natural, social, and technological complex systems. We propose a solution to the paradox by proving that scale-free degree distributions are the most likely degree distribution with the corresponding value of the structural entropy. Finally, the general framework we present in this paper is able to describe microcanonical ensembles of networks as well as canonical or hidden-variable network ensembles with significant implications for the formulation of network-constructing algorithms.

IJA: an efficient algorithm for query processing in sensor networks.

PubMed

Lee, Hyun Chang; Lee, Young Jae; Lim, Ji Hyang; Kim, Dong Hwa

2011-01-01

One of main features in sensor networks is the function that processes real time state information after gathering needed data from many domains. The component technologies consisting of each node called a sensor node that are including physical sensors, processors, actuators and power have advanced significantly over the last decade. Thanks to the advanced technology, over time sensor networks have been adopted in an all-round industry sensing physical phenomenon. However, sensor nodes in sensor networks are considerably constrained because with their energy and memory resources they have a very limited ability to process any information compared to conventional computer systems. Thus query processing over the nodes should be constrained because of their limitations. Due to the problems, the join operations in sensor networks are typically processed in a distributed manner over a set of nodes and have been studied. By way of example while simple queries, such as select and aggregate queries, in sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. Therefore, in this paper, we propose and describe an Incremental Join Algorithm (IJA) in Sensor Networks to reduce the overhead caused by moving a join pair to the final join node or to minimize the communication cost that is the main consumer of the battery when processing the distributed queries in sensor networks environments. At the same time, the simulation result shows that the proposed IJA algorithm significantly reduces the number of bytes to be moved to join nodes compared to the popular synopsis join algorithm.

IJA: An Efficient Algorithm for Query Processing in Sensor Networks

PubMed Central

Lee, Hyun Chang; Lee, Young Jae; Lim, Ji Hyang; Kim, Dong Hwa

2011-01-01

One of main features in sensor networks is the function that processes real time state information after gathering needed data from many domains. The component technologies consisting of each node called a sensor node that are including physical sensors, processors, actuators and power have advanced significantly over the last decade. Thanks to the advanced technology, over time sensor networks have been adopted in an all-round industry sensing physical phenomenon. However, sensor nodes in sensor networks are considerably constrained because with their energy and memory resources they have a very limited ability to process any information compared to conventional computer systems. Thus query processing over the nodes should be constrained because of their limitations. Due to the problems, the join operations in sensor networks are typically processed in a distributed manner over a set of nodes and have been studied. By way of example while simple queries, such as select and aggregate queries, in sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. Therefore, in this paper, we propose and describe an Incremental Join Algorithm (IJA) in Sensor Networks to reduce the overhead caused by moving a join pair to the final join node or to minimize the communication cost that is the main consumer of the battery when processing the distributed queries in sensor networks environments. At the same time, the simulation result shows that the proposed IJA algorithm significantly reduces the number of bytes to be moved to join nodes compared to the popular synopsis join algorithm. PMID:22319375

Origins of Chaos in Autonomous Boolean Networks

NASA Astrophysics Data System (ADS)

Socolar, Joshua; Cavalcante, Hugo; Gauthier, Daniel; Zhang, Rui

2010-03-01

Networks with nodes consisting of ideal Boolean logic gates are known to display either steady states, periodic behavior, or an ultraviolet catastrophe where the number of logic-transition events circulating in the network per unit time grows as a power-law. In an experiment, non-ideal behavior of the logic gates prevents the ultraviolet catastrophe and may lead to deterministic chaos. We identify certain non-ideal features of real logic gates that enable chaos in experimental networks. We find that short-pulse rejection and the asymmetry between the logic states tends to engender periodic behavior. On the other hand, a memory effect termed ``degradation'' can generate chaos. Our results strongly suggest that deterministic chaos can be expected in a large class of experimental Boolean-like networks. Such devices may find application in a variety of technologies requiring fast complex waveforms or flat power spectra. The non-ideal effects identified here also have implications for the statistics of attractors in large complex networks.

Depth estimation of features in video frames with improved feature matching technique using Kinect sensor

NASA Astrophysics Data System (ADS)

Sharma, Kajal; Moon, Inkyu; Kim, Sung Gaun

2012-10-01

Estimating depth has long been a major issue in the field of computer vision and robotics. The Kinect sensor's active sensing strategy provides high-frame-rate depth maps and can recognize user gestures and human pose. This paper presents a technique to estimate the depth of features extracted from video frames, along with an improved feature-matching method. In this paper, we used the Kinect camera developed by Microsoft, which captured color and depth images for further processing. Feature detection and selection is an important task for robot navigation. Many feature-matching techniques have been proposed earlier, and this paper proposes an improved feature matching between successive video frames with the use of neural network methodology in order to reduce the computation time of feature matching. The features extracted are invariant to image scale and rotation, and different experiments were conducted to evaluate the performance of feature matching between successive video frames. The extracted features are assigned distance based on the Kinect technology that can be used by the robot in order to determine the path of navigation, along with obstacle detection applications.

Naval War College Review. Volume 65, Number 2, Spring 2012

DTIC Science & Technology

2012-01-01

warfare doctrine that scarcely exists in today’s American military. Finally, as part of our long-standing effort to understand capabilities and...long before network-centric warfare became a central feature of joint doctrine , the Navy established a program called “Copernicus” to assimilate...exchange data if the right technology, doctrine , tactics, techniques, and procedures were in place. The importance of coalition partners effectively

Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network.

PubMed

Wu, Yonghui; Jiang, Min; Lei, Jianbo; Xu, Hua

2015-01-01

Rapid growth in electronic health records (EHRs) use has led to an unprecedented expansion of available clinical data in electronic formats. However, much of the important healthcare information is locked in the narrative documents. Therefore Natural Language Processing (NLP) technologies, e.g., Named Entity Recognition that identifies boundaries and types of entities, has been extensively studied to unlock important clinical information in free text. In this study, we investigated a novel deep learning method to recognize clinical entities in Chinese clinical documents using the minimal feature engineering approach. We developed a deep neural network (DNN) to generate word embeddings from a large unlabeled corpus through unsupervised learning and another DNN for the NER task. The experiment results showed that the DNN with word embeddings trained from the large unlabeled corpus outperformed the state-of-the-art CRF's model in the minimal feature engineering setting, achieving the highest F1-score of 0.9280. Further analysis showed that word embeddings derived through unsupervised learning from large unlabeled corpus remarkably improved the DNN with randomized embedding, denoting the usefulness of unsupervised feature learning.

Future global SLR network evolution and its impact on the terrestrial reference frame

NASA Astrophysics Data System (ADS)

Kehm, Alexander; Bloßfeld, Mathis; Pavlis, Erricos C.; Seitz, Florian

2018-06-01

Satellite laser ranging (SLR) is an important technique that contributes to the determination of terrestrial geodetic reference frames, especially to the realization of the origin and the scale of global networks. One of the major limiting factors of SLR-derived reference frame realizations is the datum accuracy which significantly suffers from the current global SLR station distribution. In this paper, the impact of a potential future development of the SLR network on the estimated datum parameters is investigated. The current status of the SLR network is compared to a simulated potential future network featuring additional stations improving the global network geometry. In addition, possible technical advancements resulting in a higher amount of observations are taken into account as well. As a result, we find that the network improvement causes a decrease in the scatter of the network translation parameters of up to 24%, and up to 20% for the scale, whereas the technological improvement causes a reduction in the scatter of up to 27% for the translations and up to 49% for the scale. The Earth orientation parameters benefit by up to 15% from both effects.

Design and implementation of a VoIP broadcasting service over embedded systems in a heterogeneous network environment.

PubMed

Leu, Jenq-Shiou; Lin, Wei-Hsiang; Hsieh, Wen-Bin; Lo, Chien-Chih

2014-01-01

As the digitization is integrated into daily life, media including video and audio are heavily transferred over the Internet nowadays. Voice-over-Internet Protocol (VoIP), the most popular and mature technology, becomes the focus attracting many researches and investments. However, most of the existing studies focused on a one-to-one communication model in a homogeneous network, instead of one-to-many broadcasting model among diverse embedded devices in a heterogeneous network. In this paper, we present the implementation of a VoIP broadcasting service on the open source-Linphone-in a heterogeneous network environment, including WiFi, 3G, and LAN networks. The proposed system featuring VoIP broadcasting over heterogeneous networks can be integrated with heterogeneous agile devices, such as embedded devices or mobile phones. VoIP broadcasting over heterogeneous networks can be integrated into modern smartphones or other embedded devices; thus when users run in a traditional AM/FM signal unreachable area, they still can receive the broadcast voice through the IP network. Also, comprehensive evaluations are conducted to verify the effectiveness of the proposed implementation.

Design and Implementation of a VoIP Broadcasting Service over Embedded Systems in a Heterogeneous Network Environment

PubMed Central

Lin, Wei-Hsiang; Hsieh, Wen-Bin; Lo, Chien-Chih

2014-01-01

As the digitization is integrated into daily life, media including video and audio are heavily transferred over the Internet nowadays. Voice-over-Internet Protocol (VoIP), the most popular and mature technology, becomes the focus attracting many researches and investments. However, most of the existing studies focused on a one-to-one communication model in a homogeneous network, instead of one-to-many broadcasting model among diverse embedded devices in a heterogeneous network. In this paper, we present the implementation of a VoIP broadcasting service on the open source—Linphone—in a heterogeneous network environment, including WiFi, 3G, and LAN networks. The proposed system featuring VoIP broadcasting over heterogeneous networks can be integrated with heterogeneous agile devices, such as embedded devices or mobile phones. VoIP broadcasting over heterogeneous networks can be integrated into modern smartphones or other embedded devices; thus when users run in a traditional AM/FM signal unreachable area, they still can receive the broadcast voice through the IP network. Also, comprehensive evaluations are conducted to verify the effectiveness of the proposed implementation. PMID:25300280

Artificial intelligence for analyzing orthopedic trauma radiographs

PubMed Central

Olczak, Jakub; Fahlberg, Niklas; Maki, Atsuto; Razavian, Ali Sharif; Jilert, Anthony; Stark, André; Sköldenberg, Olof

2017-01-01

Background and purpose — Recent advances in artificial intelligence (deep learning) have shown remarkable performance in classifying non-medical images, and the technology is believed to be the next technological revolution. So far it has never been applied in an orthopedic setting, and in this study we sought to determine the feasibility of using deep learning for skeletal radiographs. Methods — We extracted 256,000 wrist, hand, and ankle radiographs from Danderyd’s Hospital and identified 4 classes: fracture, laterality, body part, and exam view. We then selected 5 openly available deep learning networks that were adapted for these images. The most accurate network was benchmarked against a gold standard for fractures. We furthermore compared the network’s performance with 2 senior orthopedic surgeons who reviewed images at the same resolution as the network. Results — All networks exhibited an accuracy of at least 90% when identifying laterality, body part, and exam view. The final accuracy for fractures was estimated at 83% for the best performing network. The network performed similarly to senior orthopedic surgeons when presented with images at the same resolution as the network. The 2 reviewer Cohen’s kappa under these conditions was 0.76. Interpretation — This study supports the use for orthopedic radiographs of artificial intelligence, which can perform at a human level. While current implementation lacks important features that surgeons require, e.g. risk of dislocation, classifications, measurements, and combining multiple exam views, these problems have technical solutions that are waiting to be implemented for orthopedics. PMID:28681679

Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

NASA Astrophysics Data System (ADS)

Zhang, Ming; Xie, Fei; Zhao, Jing; Sun, Rui; Zhang, Lei; Zhang, Yue

2018-04-01

The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

The Role of Social Network Technologies in Online Health Promotion: A Narrative Review of Theoretical and Empirical Factors Influencing Intervention Effectiveness

PubMed Central

Kennedy, Catriona M; Buchan, Iain; Powell, John; Ainsworth, John

2015-01-01

Background Social network technologies have become part of health education and wider health promotion—either by design or happenstance. Social support, peer pressure, and information sharing in online communities may affect health behaviors. If there are positive and sustained effects, then social network technologies could increase the effectiveness and efficiency of many public health campaigns. Social media alone, however, may be insufficient to promote health. Furthermore, there may be unintended and potentially harmful consequences of inaccurate or misleading health information. Given these uncertainties, there is a need to understand and synthesize the evidence base for the use of online social networking as part of health promoting interventions to inform future research and practice. Objective Our aim was to review the research on the integration of expert-led health promotion interventions with online social networking in order to determine the extent to which the complementary benefits of each are understood and used. We asked, in particular, (1) How is effectiveness being measured and what are the specific problems in effecting health behavior change?, and (2) To what extent is the designated role of social networking grounded in theory? Methods The narrative synthesis approach to literature review was used to analyze the existing evidence. We searched the indexed scientific literature using keywords associated with health promotion and social networking. The papers included were only those making substantial study of both social networking and health promotion—either reporting the results of the intervention or detailing evidence-based plans. General papers about social networking and health were not included. Results The search identified 162 potentially relevant documents after review of titles and abstracts. Of these, 42 satisfied the inclusion criteria after full-text review. Six studies described randomized controlled trials (RCTs) evaluating the effectiveness of online social networking within health promotion interventions. Most of the trials investigated the value of a “social networking condition” in general and did not identify specific features that might play a role in effectiveness. Issues about the usability and level of uptake of interventions were more common among pilot studies, while observational studies showed positive evidence about the role of social support. A total of 20 papers showed the use of theory in the design of interventions, but authors evaluated effectiveness in only 10 papers. Conclusions More research is needed in this area to understand the actual effect of social network technologies on health promotion. More RCTs of greater length need to be conducted taking into account contextual factors such as patient characteristics and types of a social network technology. Also, more evidence is needed regarding the actual usability of online social networking and how different interface design elements may help or hinder behavior change and engagement. Moreover, it is crucial to investigate further the effect of theory on the effectiveness of this type of technology for health promotion. Research is needed linking theoretical grounding with observation and analysis of health promotion in online networks. PMID:26068087

If you build it will they come? Addressing social isolation within a technology-based HIV intervention for young black men who have sex with men.

PubMed

LeGrand, Sara; Muessig, Kathryn E; Pike, Emily C; Baltierra, Nina; Hightow-Weidman, Lisa B

2014-01-01

The rate of HIV infections among young black men who have sex with men (YBMSM) continues to rise at an alarming pace. YBMSM are particularly vulnerable to social isolation and a lack of social support due to experiences with racism and homophobia, which may have implications for sexual risk behaviors. The purpose of this study was to explore perceptions of social isolation and sense of community among YBMSM, the need for and receptivity to social networking features designed to reduce social isolation and build community within an Internet- and mobile phone-based primary and secondary HIV prevention intervention for YBMSM and to identify strategies to develop these features. Focus groups were conducted with 22 YBMSM aged 20-30 years at three sites in North Carolina. Data from the focus groups were thematically analyzed using NVivo. Feelings of social isolation and lack of a sense of community were strongly endorsed by participants with homophobia, lack of opportunities for social engagement, and a focus on sex rather than friendship in interpersonal relationships with other YBMSM cited as contributing factors. Participants were receptive to a social networking intervention designed to reduce social isolation and build community. Recommendations offered by participants to increase acceptability and usability of such features included: availability of information about healthy relationships, the ability to connect with other YBMSM and health care providers, and ensuring the site had ongoing facilitation by the study team as well as monitoring for inappropriate content. The development of a social networking feature of an HIV prevention intervention may present an opportunity to reduce social isolation, build community, and reduce risky sexual behaviors among YBMSM. The findings from this study are being used to inform the development of a social networking feature for an existing Internet- and mobile phone-based primary and secondary HIV prevention intervention for YBMSM.

If you build it will they come? Addressing social isolation within a technology-based HIV intervention for young black men who have sex with men

PubMed Central

LeGrand, Sara; Muessig, Kathryn E.; Pike, Emily C.; Baltierra, Nina; Hightow-Weidman, Lisa B.

2014-01-01

The rate of HIV infections among young black men who have sex with men (YBMSM) continues to rise at an alarming pace. YBMSM are particularly vulnerable to social isolation and a lack of social support due to experiences with racism and homophobia, which may have implications for sexual risk behaviors. The purpose of this study was to explore perceptions of social isolation and sense of community among YBMSM, the need for and receptivity to social networking features designed to reduce social isolation and build community within an internet and mobile phone-based primary and secondary HIV prevention intervention for YBMSM and to identify strategies to develop these features. Focus groups were conducted with 22 YBMSM ages 20–30 at three sites in North Carolina. Data from the focus groups were thematically analyzed using NVivo. Feelings of social isolation and lack of a sense of community were strongly endorsed by participants with homophobia, lack of opportunities for social engagement, and a focus on sex rather than friendship in interpersonal relationships with other YBMSM cited as contributing factors. Participants were receptive to a social networking intervention designed to reduce social isolation and build community. Recommendations offered by participants to increase acceptability and usability of such features included: availability of information about healthy relationships, the ability to connect with other YBMSM and health care providers, and ensuring the site had ongoing facilitation by the study team as well as monitoring for inappropriate content. The development of a social networking feature of an HIV prevention intervention may present an opportunity to reduce social isolation, build community and reduce risky sexual behaviors among YBMSM. The findings from this study are being used to inform the development of a social networking feature for an existing internet and mobile phone-based primary and secondary HIV prevention intervention for YBMSM. PMID:24617609

Dynamic Compression of the Signal in a Charge Sensitive Amplifier: From Concept to Design

NASA Astrophysics Data System (ADS)

Manghisoni, Massimo; Comotti, Daniele; Gaioni, Luigi; Ratti, Lodovico; Re, Valerio

2015-10-01

This work is concerned with the design of a low-noise Charge Sensitive Amplifier featuring a dynamic signal compression based on the non-linear features of an inversion-mode MOS capacitor. These features make the device suitable for applications where a non-linear characteristic of the front-end is required, such as in imaging instrumentation for free electron laser experiments. The aim of the paper is to discuss a methodology for the proper design of the feedback network enabling the dynamic signal compression. Starting from this compression solution, the design of a low-noise Charge Sensitive Amplifier is also discussed. The study has been carried out by referring to a 65 nm CMOS technology.

JavaScript Access to DICOM Network and Objects in Web Browser.

PubMed

Drnasin, Ivan; Grgić, Mislav; Gogić, Goran

2017-10-01

Digital imaging and communications in medicine (DICOM) 3.0 standard provides the baseline for the picture archiving and communication systems (PACS). The development of Internet and various communication media initiated demand for non-DICOM access to PACS systems. Ever-increasing utilization of the web browsers, laptops and handheld devices, as opposed to desktop applications and static organizational computers, lead to development of different web technologies. The DICOM standard officials accepted those subsequently as tools of alternative access. This paper provides an overview of the current state of development of the web access technology to the DICOM repositories. It presents a different approach of using HTML5 features of the web browsers through the JavaScript language and the WebSocket protocol by enabling real-time communication with DICOM repositories. JavaScript DICOM network library, DICOM to WebSocket proxy and a proof-of-concept web application that qualifies as a DICOM 3.0 device were developed.

A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

PubMed

Pang, Shuchao; Yu, Zhezhou; Orgun, Mehmet A

2017-03-01

Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. We propose a robust automated end-to-end classifier for biomedical images based on a domain transferred deep convolutional neural network model that shows a highly reliable and accurate performance which has been confirmed on several public biomedical image datasets. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Self-Adaptive Strategy Based on Fuzzy Control Systems for Improving Performance in Wireless Sensors Networks.

PubMed

Hernández Díaz, Vicente; Martínez, José-Fernán; Lucas Martínez, Néstor; del Toro, Raúl M

2015-09-18

The solutions to cope with new challenges that societies have to face nowadays involve providing smarter daily systems. To achieve this, technology has to evolve and leverage physical systems automatic interactions, with less human intervention. Technological paradigms like Internet of Things (IoT) and Cyber-Physical Systems (CPS) are providing reference models, architectures, approaches and tools that are to support cross-domain solutions. Thus, CPS based solutions will be applied in different application domains like e-Health, Smart Grid, Smart Transportation and so on, to assure the expected response from a complex system that relies on the smooth interaction and cooperation of diverse networked physical systems. The Wireless Sensors Networks (WSN) are a well-known wireless technology that are part of large CPS. The WSN aims at monitoring a physical system, object, (e.g., the environmental condition of a cargo container), and relaying data to the targeted processing element. The WSN communication reliability, as well as a restrained energy consumption, are expected features in a WSN. This paper shows the results obtained in a real WSN deployment, based on SunSPOT nodes, which carries out a fuzzy based control strategy to improve energy consumption while keeping communication reliability and computational resources usage among boundaries.

Self-Adaptive Strategy Based on Fuzzy Control Systems for Improving Performance in Wireless Sensors Networks

PubMed Central

Hernández Díaz, Vicente; Martínez, José-Fernán; Lucas Martínez, Néstor; del Toro, Raúl M.

2015-01-01

The solutions to cope with new challenges that societies have to face nowadays involve providing smarter daily systems. To achieve this, technology has to evolve and leverage physical systems automatic interactions, with less human intervention. Technological paradigms like Internet of Things (IoT) and Cyber-Physical Systems (CPS) are providing reference models, architectures, approaches and tools that are to support cross-domain solutions. Thus, CPS based solutions will be applied in different application domains like e-Health, Smart Grid, Smart Transportation and so on, to assure the expected response from a complex system that relies on the smooth interaction and cooperation of diverse networked physical systems. The Wireless Sensors Networks (WSN) are a well-known wireless technology that are part of large CPS. The WSN aims at monitoring a physical system, object, (e.g., the environmental condition of a cargo container), and relaying data to the targeted processing element. The WSN communication reliability, as well as a restrained energy consumption, are expected features in a WSN. This paper shows the results obtained in a real WSN deployment, based on SunSPOT nodes, which carries out a fuzzy based control strategy to improve energy consumption while keeping communication reliability and computational resources usage among boundaries. PMID:26393612

miRNet - dissecting miRNA-target interactions and functional associations through network-based visual analysis

PubMed Central

Fan, Yannan; Siklenka, Keith; Arora, Simran K.; Ribeiro, Paula; Kimmins, Sarah; Xia, Jianguo

2016-01-01

MicroRNAs (miRNAs) can regulate nearly all biological processes and their dysregulation is implicated in various complex diseases and pathological conditions. Recent years have seen a growing number of functional studies of miRNAs using high-throughput experimental technologies, which have produced a large amount of high-quality data regarding miRNA target genes and their interactions with small molecules, long non-coding RNAs, epigenetic modifiers, disease associations, etc. These rich sets of information have enabled the creation of comprehensive networks linking miRNAs with various biologically important entities to shed light on their collective functions and regulatory mechanisms. Here, we introduce miRNet, an easy-to-use web-based tool that offers statistical, visual and network-based approaches to help researchers understand miRNAs functions and regulatory mechanisms. The key features of miRNet include: (i) a comprehensive knowledge base integrating high-quality miRNA-target interaction data from 11 databases; (ii) support for differential expression analysis of data from microarray, RNA-seq and quantitative PCR; (iii) implementation of a flexible interface for data filtering, refinement and customization during network creation; (iv) a powerful fully featured network visualization system coupled with enrichment analysis. miRNet offers a comprehensive tool suite to enable statistical analysis and functional interpretation of various data generated from current miRNA studies. miRNet is freely available at http://www.mirnet.ca. PMID:27105848

Ion trap architectures and new directions

NASA Astrophysics Data System (ADS)

Siverns, James D.; Quraishi, Qudsia

2017-12-01

Trapped ion technology has seen advances in performance, robustness and versatility over the last decade. With increasing numbers of trapped ion groups worldwide, a myriad of trap architectures are currently in use. Applications of trapped ions include: quantum simulation, computing and networking, time standards and fundamental studies in quantum dynamics. Design of such traps is driven by these various research aims, but some universally desirable properties have lead to the development of ion trap foundries. Additionally, the excellent control achievable with trapped ions and the ability to do photonic readout has allowed progress on quantum networking using entanglement between remotely situated ion-based nodes. Here, we present a selection of trap architectures currently in use by the community and present their most salient characteristics, identifying features particularly suited for quantum networking. We also discuss our own in-house research efforts aimed at long-distance trapped ion networking.

Design of a SIP device cooperation system on OSGi service platforms

NASA Astrophysics Data System (ADS)

Takayama, Youji; Koita, Takahiro; Sato, Kenya

2007-12-01

Home networks feature such various technologies as protocols, specifications, and middleware, including HTTP, UPnP, and Jini. A service platform is required to handle such technologies to enable them to cooperate with different devices. The OSGi service platform, which meets the requirements based on service-oriented architecture, is designed and standardized by OSGi Alliance and consists of two parts: one OSGi Framework and bundles. On the OSGi service platform, APIs are defined as services that can handle these technologies and are implemented in the bundle. By using the OSGi Framework with bundles, various technologies can cooperate with each other. On the other hand, in IP networks, Session Initiation Protocol (SIP) is often used in device cooperation services to resolve an IP address, control a session between two or more devices, and easily exchange the statuses of devices. However, since many existing devices do not correspond to SIP, it cannot be used for device cooperation services. A device that does not correspond to SIP is called an unSIP device. This paper proposes and implements a prototype system that enables unSIP devices to correspond to SIP. For unSIP devices, the proposed system provides device cooperation services with SIP.

A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network

PubMed Central

Salari, Nader; Shohaimi, Shamarina; Najafi, Farid; Nallappan, Meenakshii; Karishnarajah, Isthrinayagy

2014-01-01

Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. The purpose is benefitting from the synergies obtained from combining these technologies for the development of classification models. Such a combination creates an opportunity to invest in the strength of each algorithm, and is an approach to make up for their deficiencies. To develop proposed model, with the aim of obtaining the best array of features, first, feature ranking techniques such as the Fisher's discriminant ratio and class separability criteria were used to prioritize features. Second, the obtained results that included arrays of the top-ranked features were used as the initial population of a genetic algorithm to produce optimum arrays of features. Third, using a modified k-Nearest Neighbor method as well as an improved method of backpropagation neural networks, the classification process was advanced based on optimum arrays of the features selected by genetic algorithms. The performance of the proposed model was compared with thirteen well-known classification models based on seven datasets. Furthermore, the statistical analysis was performed using the Friedman test followed by post-hoc tests. The experimental findings indicated that the novel proposed hybrid model resulted in significantly better classification performance compared with all 13 classification methods. Finally, the performance results of the proposed model was benchmarked against the best ones reported as the state-of-the-art classifiers in terms of classification accuracy for the same data sets. The substantial findings of the comprehensive comparative study revealed that performance of the proposed model in terms of classification accuracy is desirable, promising, and competitive to the existing state-of-the-art classification models. PMID:25419659

Algorithm research for user trajectory matching across social media networks based on paragraph2vec

NASA Astrophysics Data System (ADS)

Xu, Qian; Chen, Hongchang; Zhi, Hongxin; Wang, Yanchuan

2018-04-01

Identifying users across different social media networks (SMN) is to link accounts of the same user that belong to the same individual across SMNs. The problem is fundamental and important, and its results can benefit many applications such as cross SMN user modeling and recommendation. With the development of GPS technology and mobile communication, more and more social networks provide location services. This provides a new opportunity for cross SMN user identification. In this paper, we solve cross SMN user identification problem in an unsupervised manner by utilizing user trajectory data in SMNs. A paragraph2vec based algorithm is proposed in which location sequence feature of user trajectory is captured in temporal and spatial dimensions. Our experimental results validate the effectiveness and efficiency of our algorithm.

Pervasive Home Environments

NASA Astrophysics Data System (ADS)

Bull, P.; Limb, R.; Payne, R.

An increasing number of computers and other equipment, such as games consoles and multimedia appliances for the home, have networking capability. The rapid growth of broadband in the home is also fuelling the demand for people to network their homes. In the near future we will see a number of market sectors trying to 'own' the home by providing gateways either from the traditional ISP or from games and other service providers. The consumer is bombarded with attractive advertising to acquire the latest technological advances, but is left with a plethora of different appliances, which have a bewildering range of requirements and features in terms of networking, user interface, and higher-level communications protocols. In many cases, these are proprietary, preventing interworking. Such technical and usability anarchy confuses the consumer and could ultimately suppress market adoption.

Metro Optical Networks for Homeland Security

NASA Astrophysics Data System (ADS)

Bechtel, James H.

Metro optical networks provide an enticing opportunity for strengthening homeland security. Many existing and emerging fiber-optic networks can be adapted for enhanced security applications. Applications include airports, theme parks, sports venues, and border surveillance systems. Here real-time high-quality video and captured images can be collected, transported, processed, and stored for security applications. Video and data collection are important also at correctional facilities, courts, infrastructure (e.g., dams, bridges, railroads, reservoirs, power stations), and at military and other government locations. The scaling of DWDM-based networks allows vast amounts of data to be collected and transported including biometric features of individuals at security check points. Here applications will be discussed along with potential solutions and challenges. Examples of solutions to these problems are given. This includes a discussion of metropolitan aggregation platforms for voice, video, and data that are SONET compliant for use in SONET networks and the use of DWDM technology for scaling and transporting a variety of protocols. Element management software allows not only network status monitoring, but also provides optimized allocation of network resources through the use of optical switches or electrical cross connects.

Scientific Collaboration in Chinese Nursing Research: A Social Network Analysis Study.

PubMed

Hou, Xiao-Ni; Hao, Yu-Fang; Cao, Jing; She, Yan-Chao; Duan, Hong-Mei

2016-01-01

Collaboration has become very important in research and in technological progress. Coauthorship networks in different fields have been intensively studied as an important type of collaboration in recent years. Yet there are few published reports about collaboration in the field of nursing. This article aimed to reveal the status and identify the key features of collaboration in the field of nursing in China. Using data from the top 10 nursing journals in China from 2003 to 2013, we constructed a nursing scientific coauthorship network using social network analysis. We found that coauthorship was a common phenomenon in the Chinese nursing field. A coauthorship network with 228 subnetworks formed by 1428 nodes was constructed. The network was relatively loose, and most subnetworks were of small scales. Scholars from Shanghai and from military medical system were at the center of the Chinese nursing scientific coauthorship network. We identified the authors' positions and influences according to the research output and centralities of each author. We also analyzed the microstructure and the evolution over time of the maximum subnetwork.

A New Approach to Develop Computer-aided Diagnosis Scheme of Breast Mass Classification Using Deep Learning Technology

PubMed Central

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

PURPOSE To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. METHODS An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. RESULTS The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. CONCLUSIONS This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process. PMID:28436410

A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.

PubMed

Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

2017-01-01

To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process.

Cyber Network Mission Dependencies

DTIC Science & Technology

2015-09-18

May Also Like” (YMAL) features of popular services like Amazon and Netflix . A mockup interface is shown in Figure 6. A supply agent would enter the...without causing serious harm to the execution of the mission. This idea is based on the technology of the Simian Army, implemented by Netflix and used...very successfully both there and at Amazon [15]. The Netflix Simian Army is designed to force developers to create resilient and robust software

Learning a Language with Web 2.0: Exploring the Use of Social Networking Features of Foreign Language Learning Websites

ERIC Educational Resources Information Center

Stevenson, Megan P.; Liu, Min

2010-01-01

This paper presents the results of an online survey and a usability test performed on three foreign language learning websites that use Web 2.0 technology. The online survey was conducted to gain an understanding of how current users of language learning websites use them for learning and social purposes. The usability test was conducted to gain…

Efficient Feature Extraction and Likelihood Fusion for Vehicle Tracking in Low Frame Rate Airborne Video

DTIC Science & Technology

2010-07-01

imagery, persistent sensor array I. Introduction New device fabrication technologies and heterogeneous embedded processors have led to the emergence of a...geometric occlusions between target and sensor , motion blur, urban scene complexity, and high data volumes. In practical terms the targets are small...distributed airborne narrow-field-of-view video sensor networks. Airborne camera arrays combined with com- putational photography techniques enable the

Model-Driven Approach for Body Area Network Application Development.

PubMed

Venčkauskas, Algimantas; Štuikys, Vytautas; Jusas, Nerijus; Burbaitė, Renata

2016-05-12

This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application.

Model-Driven Approach for Body Area Network Application Development

PubMed Central

Venčkauskas, Algimantas; Štuikys, Vytautas; Jusas, Nerijus; Burbaitė, Renata

2016-01-01

This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application. PMID:27187394

Use of Multimedia or Mobile Devices By Adolescents for Health Promotion and Disease Prevention: A Literature Review.

PubMed

Geckle, Joan

2016-01-01

The purpose of this literature review is to summarize studies of the use of multimedia technology by adolescents to engage in their healthcare promotion and disease prevention. A systematic literature review of relevant peer-reviewed research published between 2009 and 2014 was performed. The 16 articles reviewed were a combination of quantitative and mixed-method methodology based on the efficacy of multimedia, mobile technology, Short Messaging Services (SMS) texting, and social networking (e.g., Facebook®), to engage adolescents ages 10 to 20 years in health promotion and disease prevention. Although adolescents have high attrition rates in the studies, they demonstrated advantages in using SMS texting features and social networking, especially the chat function, in relation to health promotion and disease prevention. Some small gains were noticed in health promotion and disease prevention in the majority of the studies, though some were not significant due to attrition. Additional research, especially nursing research, is necessary. Mobile and multimedia technology allows for a promising correlation between adolescents and increased healthcare knowledge, health promotion, and disease prevention.

Integrated heart failure telemonitoring system for homecare.

PubMed

Lobodzinski, S Suave; Jadalla, Ahlam A

2010-01-01

The integrated telemonitoring system (ITS) for homecare has been designed to improve quality of care as measured by increased nursing productivity, improved patients' clinical and behavioral outcomes and reduction of cost. The system incorporates managerial, organizational, operational and clinical tasks optimized for delivery of quality care through telemonitoring. A secure, multi-modal computer network that integrates homecare nurses, patients and those who care into one seamless environment has been developed. The network brings together a new generation of small, hand-held, wireless terminals used by nurses and patients with a HIPPA-compliant electronic patient record system at the caregiver's site. Wireless terminals use Gobi multi-standard networking technology for connectivity to any available wireless network. The unique features of ITS include a) picture recognition technology capable of extracting numeric data from in-home physiological signal monitor displays that include blood pressure, weight, oxygen saturation, transmission of lung sounds, and capturing echocardiography and electrocardiography data from mobile units; b) in-home caregiver-assisted interactive examinations of signs and symptoms that include visual impressions of ankle swelling, jugular vein distension measurement, and weight gain; c) video-conference capability, facilitating face-to-face two-way communication of nursing personnel with the patients. The ITS network has been designed to improve patients' clinical and behavioral outcomes, increase nursing productivity, and reduce the cost of homecare. Patients' co-operation and compliance has been achieved through use of easy-to-use videoconferencing terminals.

Ontology Alignment Architecture for Semantic Sensor Web Integration

PubMed Central

Fernandez, Susel; Marsa-Maestre, Ivan; Velasco, Juan R.; Alarcos, Bernardo

2013-01-01

Sensor networks are a concept that has become very popular in data acquisition and processing for multiple applications in different fields such as industrial, medicine, home automation, environmental detection, etc. Today, with the proliferation of small communication devices with sensors that collect environmental data, semantic Web technologies are becoming closely related with sensor networks. The linking of elements from Semantic Web technologies with sensor networks has been called Semantic Sensor Web and has among its main features the use of ontologies. One of the key challenges of using ontologies in sensor networks is to provide mechanisms to integrate and exchange knowledge from heterogeneous sources (that is, dealing with semantic heterogeneity). Ontology alignment is the process of bringing ontologies into mutual agreement by the automatic discovery of mappings between related concepts. This paper presents a system for ontology alignment in the Semantic Sensor Web which uses fuzzy logic techniques to combine similarity measures between entities of different ontologies. The proposed approach focuses on two key elements: the terminological similarity, which takes into account the linguistic and semantic information of the context of the entity's names, and the structural similarity, based on both the internal and relational structure of the concepts. This work has been validated using sensor network ontologies and the Ontology Alignment Evaluation Initiative (OAEI) tests. The results show that the proposed techniques outperform previous approaches in terms of precision and recall. PMID:24051523

Ontology alignment architecture for semantic sensor Web integration.

PubMed

Fernandez, Susel; Marsa-Maestre, Ivan; Velasco, Juan R; Alarcos, Bernardo

2013-09-18

Sensor networks are a concept that has become very popular in data acquisition and processing for multiple applications in different fields such as industrial, medicine, home automation, environmental detection, etc. Today, with the proliferation of small communication devices with sensors that collect environmental data, semantic Web technologies are becoming closely related with sensor networks. The linking of elements from Semantic Web technologies with sensor networks has been called Semantic Sensor Web and has among its main features the use of ontologies. One of the key challenges of using ontologies in sensor networks is to provide mechanisms to integrate and exchange knowledge from heterogeneous sources (that is, dealing with semantic heterogeneity). Ontology alignment is the process of bringing ontologies into mutual agreement by the automatic discovery of mappings between related concepts. This paper presents a system for ontology alignment in the Semantic Sensor Web which uses fuzzy logic techniques to combine similarity measures between entities of different ontologies. The proposed approach focuses on two key elements: the terminological similarity, which takes into account the linguistic and semantic information of the context of the entity's names, and the structural similarity, based on both the internal and relational structure of the concepts. This work has been validated using sensor network ontologies and the Ontology Alignment Evaluation Initiative (OAEI) tests. The results show that the proposed techniques outperform previous approaches in terms of precision and recall.

Concurrent evolution of feature extractors and modular artificial neural networks

NASA Astrophysics Data System (ADS)

Hannak, Victor; Savakis, Andreas; Yang, Shanchieh Jay; Anderson, Peter

2009-05-01

This paper presents a new approach for the design of feature-extracting recognition networks that do not require expert knowledge in the application domain. Feature-Extracting Recognition Networks (FERNs) are composed of interconnected functional nodes (feurons), which serve as feature extractors, and are followed by a subnetwork of traditional neural nodes (neurons) that act as classifiers. A concurrent evolutionary process (CEP) is used to search the space of feature extractors and neural networks in order to obtain an optimal recognition network that simultaneously performs feature extraction and recognition. By constraining the hill-climbing search functionality of the CEP on specific parts of the solution space, i.e., individually limiting the evolution of feature extractors and neural networks, it was demonstrated that concurrent evolution is a necessary component of the system. Application of this approach to a handwritten digit recognition task illustrates that the proposed methodology is capable of producing recognition networks that perform in-line with other methods without the need for expert knowledge in image processing.

Continuous Dropout.

PubMed

Shen, Xu; Tian, Xinmei; Liu, Tongliang; Xu, Fang; Tao, Dacheng

2017-10-03

Dropout has been proven to be an effective algorithm for training robust deep networks because of its ability to prevent overfitting by avoiding the co-adaptation of feature detectors. Current explanations of dropout include bagging, naive Bayes, regularization, and sex in evolution. According to the activation patterns of neurons in the human brain, when faced with different situations, the firing rates of neurons are random and continuous, not binary as current dropout does. Inspired by this phenomenon, we extend the traditional binary dropout to continuous dropout. On the one hand, continuous dropout is considerably closer to the activation characteristics of neurons in the human brain than traditional binary dropout. On the other hand, we demonstrate that continuous dropout has the property of avoiding the co-adaptation of feature detectors, which suggests that we can extract more independent feature detectors for model averaging in the test stage. We introduce the proposed continuous dropout to a feedforward neural network and comprehensively compare it with binary dropout, adaptive dropout, and DropConnect on Modified National Institute of Standards and Technology, Canadian Institute for Advanced Research-10, Street View House Numbers, NORB, and ImageNet large scale visual recognition competition-12. Thorough experiments demonstrate that our method performs better in preventing the co-adaptation of feature detectors and improves test performance.

New computing systems, future computing environment, and their implications on structural analysis and design

NASA Technical Reports Server (NTRS)

Noor, Ahmed K.; Housner, Jerrold M.

1993-01-01

Recent advances in computer technology that are likely to impact structural analysis and design of flight vehicles are reviewed. A brief summary is given of the advances in microelectronics, networking technologies, and in the user-interface hardware and software. The major features of new and projected computing systems, including high performance computers, parallel processing machines, and small systems, are described. Advances in programming environments, numerical algorithms, and computational strategies for new computing systems are reviewed. The impact of the advances in computer technology on structural analysis and the design of flight vehicles is described. A scenario for future computing paradigms is presented, and the near-term needs in the computational structures area are outlined.

Networks: A Review of Their Technology, Architecture, and Implementation.

ERIC Educational Resources Information Center

Learn, Larry L.

1988-01-01

This overview of network-related technologies covers network elements, analog and digital signals, transmission media and their characteristics, equipment certification, multiplexing, network types, access technologies, network architectures local-area network technologies and attributes, protocols, internetworking, fiber optics versus satellites,…

GENET note no. 1

NASA Technical Reports Server (NTRS)

Yeh, J. W.

1971-01-01

The general features of the GENET system for simulating networks are described. A set of features is presented which are desirable for network simulations and which are expected to be achieved by this system. Among these features are: (1) two level network modeling; and (2) problem oriented operations. Several typical network systems are modeled in GENET framework to illustrate various of the features and to show its applicability.

HIMSS Venture+ Forum and HX360 Provide Industry View of Health Technology Innovation, Startup and Investment Activity; Advancing the New Model of Care.

PubMed

Burde, Howard A; Scarfo, Richard

2015-01-01

Presented by HIMSS, the Venture+ Forum program and pitch competition provides a 360-degree view on health technology investing and today's top innovative companies. It features exciting 3-minute pitch presentations from emerging and growth-stage companies, investor panels and a networking reception. Recent Venture+ Forum winners include TowerView Health, Prima-Temp, ActuaiMeds and M3 Clinician. As an industry catalyst for health IT innovation and business-building resource for growing companies and emerging technology solutions, HIMSS has co-developed with A VIA, a new initiative that addresses how emerging technologies, health system business model changes and investment will transform the delivery of care. HX360 engages senior healthcare leaders, innovation teams, investors and entrepreneurs around the vision of transforming healthcare delivery by leveraging technology, process and structure.

Learning Efficient Spatial-Temporal Gait Features with Deep Learning for Human Identification.

PubMed

Liu, Wu; Zhang, Cheng; Ma, Huadong; Li, Shuangqun

2018-02-06

The integration of the latest breakthroughs in bioinformatics technology from one side and artificial intelligence from another side, enables remarkable advances in the fields of intelligent security guard computational biology, healthcare, and so on. Among them, biometrics based automatic human identification is one of the most fundamental and significant research topic. Human gait, which is a biometric features with the unique capability, has gained significant attentions as the remarkable characteristics of remote accessed, robust and security in the biometrics based human identification. However, the existed methods cannot well handle the indistinctive inter-class differences and large intra-class variations of human gait in real-world situation. In this paper, we have developed an efficient spatial-temporal gait features with deep learning for human identification. First of all, we proposed a gait energy image (GEI) based Siamese neural network to automatically extract robust and discriminative spatial gait features for human identification. Furthermore, we exploit the deep 3-dimensional convolutional networks to learn the human gait convolutional 3D (C3D) as the temporal gait features. Finally, the GEI and C3D gait features are embedded into the null space by the Null Foley-Sammon Transform (NFST). In the new space, the spatial-temporal features are sufficiently combined with distance metric learning to drive the similarity metric to be small for pairs of gait from the same person, and large for pairs from different persons. Consequently, the experiments on the world's largest gait database show our framework impressively outperforms state-of-the-art methods.

An analysis of the influence of deep neural network (DNN) topology in bottleneck feature based language recognition.

PubMed

Lozano-Diez, Alicia; Zazo, Ruben; Toledano, Doroteo T; Gonzalez-Rodriguez, Joaquin

2017-01-01

Language recognition systems based on bottleneck features have recently become the state-of-the-art in this research field, showing its success in the last Language Recognition Evaluation (LRE 2015) organized by NIST (U.S. National Institute of Standards and Technology). This type of system is based on a deep neural network (DNN) trained to discriminate between phonetic units, i.e. trained for the task of automatic speech recognition (ASR). This DNN aims to compress information in one of its layers, known as bottleneck (BN) layer, which is used to obtain a new frame representation of the audio signal. This representation has been proven to be useful for the task of language identification (LID). Thus, bottleneck features are used as input to the language recognition system, instead of a classical parameterization of the signal based on cepstral feature vectors such as MFCCs (Mel Frequency Cepstral Coefficients). Despite the success of this approach in language recognition, there is a lack of studies analyzing in a systematic way how the topology of the DNN influences the performance of bottleneck feature-based language recognition systems. In this work, we try to fill-in this gap, analyzing language recognition results with different topologies for the DNN used to extract the bottleneck features, comparing them and against a reference system based on a more classical cepstral representation of the input signal with a total variability model. This way, we obtain useful knowledge about how the DNN configuration influences bottleneck feature-based language recognition systems performance.

Holographic neural networks versus conventional neural networks: a comparative evaluation for the classification of landmine targets in ground-penetrating radar images

NASA Astrophysics Data System (ADS)

Mudigonda, Naga R.; Kacelenga, Ray; Edwards, Mark

2004-09-01

This paper evaluates the performance of a holographic neural network in comparison with a conventional feedforward backpropagation neural network for the classification of landmine targets in ground penetrating radar images. The data used in the study was acquired from four different test sites using the landmine detection system developed by General Dynamics Canada Ltd., in collaboration with the Defense Research and Development Canada, Suffield. A set of seven features extracted for each detected alarm is used as stimulus inputs for the networks. The recall responses of the networks are then evaluated against the ground truth to declare true or false detections. The area computed under the receiver operating characteristic curve is used for comparative purposes. With a large dataset comprising of data from multiple sites, both the holographic and conventional networks showed comparable trends in recall accuracies with area values of 0.88 and 0.87, respectively. By using independent validation datasets, the holographic network"s generalization performance was observed to be better (mean area = 0.86) as compared to the conventional network (mean area = 0.82). Despite the widely publicized theoretical advantages of the holographic technology, use of more than the required number of cortical memory elements resulted in an over-fitting phenomenon of the holographic network.

Empirical analysis of online social networks in the age of Web 2.0

NASA Astrophysics Data System (ADS)

Fu, Feng; Liu, Lianghuan; Wang, Long

2008-01-01

Today the World Wide Web is undergoing a subtle but profound shift to Web 2.0, to become more of a social web. The use of collaborative technologies such as blogs and social networking site (SNS) leads to instant online community in which people communicate rapidly and conveniently with each other. Moreover, there are growing interest and concern regarding the topological structure of these new online social networks. In this paper, we present empirical analysis of statistical properties of two important Chinese online social networks-a blogging network and an SNS open to college students. They are both emerging in the age of Web 2.0. We demonstrate that both networks possess small-world and scale-free features already observed in real-world and artificial networks. In addition, we investigate the distribution of topological distance. Furthermore, we study the correlations between degree (in/out) and degree (in/out), clustering coefficient and degree, popularity (in terms of number of page views) and in-degree (for the blogging network), respectively. We find that the blogging network shows disassortative mixing pattern, whereas the SNS network is an assortative one. Our research may help us to elucidate the self-organizing structural characteristics of these online social networks embedded in technical forms.

BASiNET-BiologicAl Sequences NETwork: a case study on coding and non-coding RNAs identification.

PubMed

Ito, Eric Augusto; Katahira, Isaque; Vicente, Fábio Fernandes da Rocha; Pereira, Luiz Filipe Protasio; Lopes, Fabrício Martins

2018-06-05

With the emergence of Next Generation Sequencing (NGS) technologies, a large volume of sequence data in particular de novo sequencing was rapidly produced at relatively low costs. In this context, computational tools are increasingly important to assist in the identification of relevant information to understand the functioning of organisms. This work introduces BASiNET, an alignment-free tool for classifying biological sequences based on the feature extraction from complex network measurements. The method initially transform the sequences and represents them as complex networks. Then it extracts topological measures and constructs a feature vector that is used to classify the sequences. The method was evaluated in the classification of coding and non-coding RNAs of 13 species and compared to the CNCI, PLEK and CPC2 methods. BASiNET outperformed all compared methods in all adopted organisms and datasets. BASiNET have classified sequences in all organisms with high accuracy and low standard deviation, showing that the method is robust and non-biased by the organism. The proposed methodology is implemented in open source in R language and freely available for download at https://cran.r-project.org/package=BASiNET.

Upgrading of data acquisition software for centralized radiation monitoring system in Malaysian Nuclear Agency

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

Yussup, F., E-mail: nolida@nm.gov.my; Ibrahim, M. M., E-mail: maslina-i@nm.gov.my; Soh, S. C.

With the growth of technology, many devices and equipments can be connected to the network and internet to enable online data acquisition for real-time data monitoring and control from monitoring devices located at remote sites. Centralized radiation monitoring system (CRMS) is a system that enables area radiation level at various locations in Malaysian Nuclear Agency (Nuklear Malaysia) to be monitored centrally by using a web browser. The Local Area Network (LAN) in Nuclear Malaysia is utilized in CRMS as a communication media for data acquisition of the area radiation levels from radiation detectors. The development of the system involves devicemore » configuration, wiring, network and hardware installation, software and web development. This paper describes the software upgrading on the system server that is responsible to acquire and record the area radiation readings from the detectors. The recorded readings are called in a web programming to be displayed on a website. Besides the main feature which is acquiring the area radiation levels in Nuclear Malaysia centrally, the upgrading involves new features such as uniform time interval for data recording and exporting, warning system and dose triggering.« less

Unattended network operations technology assessment study. Technical support for defining advanced satellite systems concepts

NASA Technical Reports Server (NTRS)

Price, Kent M.; Holdridge, Mark; Odubiyi, Jide; Jaworski, Allan; Morgan, Herbert K.

1991-01-01

The results are summarized of an unattended network operations technology assessment study for the Space Exploration Initiative (SEI). The scope of the work included: (1) identified possible enhancements due to the proposed Mars communications network; (2) identified network operations on Mars; (3) performed a technology assessment of possible supporting technologies based on current and future approaches to network operations; and (4) developed a plan for the testing and development of these technologies. The most important results obtained are as follows: (1) addition of a third Mars Relay Satellite (MRS) and MRS cross link capabilities will enhance the network's fault tolerance capabilities through improved connectivity; (2) network functions can be divided into the six basic ISO network functional groups; (3) distributed artificial intelligence technologies will augment more traditional network management technologies to form the technological infrastructure of a virtually unattended network; and (4) a great effort is required to bring the current network technology levels for manned space communications up to the level needed for an automated fault tolerance Mars communications network.

BioNet Digital Communications Framework

NASA Technical Reports Server (NTRS)

Gifford, Kevin; Kuzminsky, Sebastian; Williams, Shea

2010-01-01

BioNet v2 is a peer-to-peer middleware that enables digital communication devices to talk to each other. It provides a software development framework, standardized application, network-transparent device integration services, a flexible messaging model, and network communications for distributed applications. BioNet is an implementation of the Constellation Program Command, Control, Communications and Information (C3I) Interoperability specification, given in CxP 70022-01. The system architecture provides the necessary infrastructure for the integration of heterogeneous wired and wireless sensing and control devices into a unified data system with a standardized application interface, providing plug-and-play operation for hardware and software systems. BioNet v2 features a naming schema for mobility and coarse-grained localization information, data normalization within a network-transparent device driver framework, enabling of network communications to non-IP devices, and fine-grained application control of data subscription band width usage. BioNet directly integrates Disruption Tolerant Networking (DTN) as a communications technology, enabling networked communications with assets that are only intermittently connected including orbiting relay satellites and planetary rover vehicles.

Information Technology. DOD Needs to Strengthen Management of Its Statutorily Mandated Software and System Process Improvement Efforts

DTIC Science & Technology

2009-09-01

NII)/CIO Assistant Secretary of Defense for Networks and Information Integration/Chief Information Officer CMMI Capability Maturity Model...a Web-based portal to share knowledge about software process-related methodologies, such as the SEI’s Capability Maturity Model Integration ( CMMI ...19 SEI’s IDEALSM model, and Lean Six Sigma.20 For example, the portal features content areas such as software acquisition management, the SEI CMMI

Optical Access Networks

NASA Astrophysics Data System (ADS)

Zheng, Jun; Ansari, Nirwan

2005-02-01

Call for Papers: Optical Access Networks With the wide deployment of fiber-optic technology over the past two decades, we have witnessed a tremendous growth of bandwidth capacity in the backbone networks of today's telecommunications infrastructure. However, access networks, which cover the "last-mile" areas and serve numerous residential and small business users, have not been scaled up commensurately. The local subscriber lines for telephone and cable television are still using twisted pairs and coaxial cables. Most residential connections to the Internet are still through dial-up modems operating at a low speed on twisted pairs. As the demand for access bandwidth increases with emerging high-bandwidth applications, such as distance learning, high-definition television (HDTV), and video on demand (VoD), the last-mile access networks have become a bandwidth bottleneck in today's telecommunications infrastructure. To ease this bottleneck, it is imperative to provide sufficient bandwidth capacity in the access networks to open the bottleneck and thus present more opportunities for the provisioning of multiservices. Optical access solutions promise huge bandwidth to service providers and low-cost high-bandwidth services to end users and are therefore widely considered the technology of choice for next-generation access networks. To realize the vision of optical access networks, however, many key issues still need to be addressed, such as network architectures, signaling protocols, and implementation standards. The major challenges lie in the fact that an optical solution must be not only robust, scalable, and flexible, but also implemented at a low cost comparable to that of existing access solutions in order to increase the economic viability of many potential high-bandwidth applications. In recent years, optical access networks have been receiving tremendous attention from both academia and industry. A large number of research activities have been carried out or are now underway this hot area. The purpose of this feature issue is to expose the networking community to the latest research breakthroughs and progresses in the area of optical access networks.

Morphological self-organizing feature map neural network with applications to automatic target recognition

NASA Astrophysics Data System (ADS)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

CAPILLARY NETWORK ANOMALIES IN BRANCH RETINAL VEIN OCCLUSION ON OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY.

PubMed

Rispoli, Marco; Savastano, Maria Cristina; Lumbroso, Bruno

2015-11-01

To analyze the foveal microvasculature features in eyes with branch retinal vein occlusion (BRVO) using optical coherence tomography angiography based on split spectrum amplitude decorrelation angiography technology. A total of 10 BRVO eyes (mean age 64.2 ± 8.02 range between 52 years and 76 years) were evaluated by optical coherence tomography angiography (XR-Avanti; Optovue). The macular angiography scan protocol covered a 3 mm × 3 mm area. The focus of angiography analysis were two retinal layers: superficial vascular network and deep vascular network. The following vascular morphological congestion parameters were assessed in the vein occlusion area in both the superficial and deep networks: foveal avascular zone enlargement, capillary non-perfusion occurrence, microvascular abnormalities appearance, and vascular congestion signs. Image analyses were performed by 2 masked observers and interobserver agreement of image analyses was 0.90 (κ = 0.225, P < 0.01). In both superficial and deep network of BRVO, a decrease in capillary density with foveal avascular zone enlargement, capillary non-perfusion occurrence, and microvascular abnormalities appearance was observed (P < 0.01). The deep network showed the main vascular congestion at the boundary between healthy and nonperfused retina. Optical coherence tomography angiography in BRVO allows to detect foveal avascular zone enlargement, capillary nonperfusion, microvascular abnormalities, and vascular congestion signs both in the superficial and deep capillary network in all eyes. Optical coherence tomography angiography technology is a potential clinical tool for BRVO diagnosis and follow-up, providing stratigraphic vascular details that have not been previously observed by standard fluorescein angiography. The normal retinal vascular nets and areas of nonperfusion and congestion can be identified at various retinal levels. Optical coherence tomography angiography provides noninvasive images of the retinal capillaries and vascular networks.

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

PubMed Central

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

2014-01-01

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

NaNet: a configurable NIC bridging the gap between HPC and real-time HEP GPU computing

NASA Astrophysics Data System (ADS)

Lonardo, A.; Ameli, F.; Ammendola, R.; Biagioni, A.; Cotta Ramusino, A.; Fiorini, M.; Frezza, O.; Lamanna, G.; Lo Cicero, F.; Martinelli, M.; Neri, I.; Paolucci, P. S.; Pastorelli, E.; Pontisso, L.; Rossetti, D.; Simeone, F.; Simula, F.; Sozzi, M.; Tosoratto, L.; Vicini, P.

2015-04-01

NaNet is a FPGA-based PCIe Network Interface Card (NIC) design with GPUDirect and Remote Direct Memory Access (RDMA) capabilities featuring a configurable and extensible set of network channels. The design currently supports both standard—Gbe (1000BASE-T) and 10GbE (10Base-R)—and custom—34 Gbps APElink and 2.5 Gbps deterministic latency KM3link—channels, but its modularity allows for straightforward inclusion of other link technologies. The GPUDirect feature combined with a transport layer offload module and a data stream processing stage makes NaNet a low-latency NIC suitable for real-time GPU processing. In this paper we describe the NaNet architecture and its performances, exhibiting two of its use cases: the GPU-based low-level trigger for the RICH detector in the NA62 experiment at CERN and the on-/off-shore data transport system for the KM3NeT-IT underwater neutrino telescope.

Distributed rewiring model for complex networking: The effect of local rewiring rules on final structural properties.

PubMed

López Chavira, Magali Alexander; Marcelín-Jiménez, Ricardo

2017-01-01

The study of complex networks has become an important subject over the last decades. It has been shown that these structures have special features, such as their diameter, or their average path length, which in turn are the explanation of some functional properties in a system such as its fault tolerance, its fragility before attacks, or the ability to support routing procedures. In the present work, we study some of the forces that help a network to evolve to the point where structural properties are settled. Although our work is mainly focused on the possibility of applying our ideas to Information and Communication Technologies systems, we consider that our results may contribute to understanding different scenarios where complex networks have become an important modeling tool. Using a discrete event simulator, we get each node to discover the shortcuts that may connect it with regions away from its local environment. Based on this partial knowledge, each node can rewire some of its links, which allows modifying the topology of the entire underlying graph to achieve new structural properties. We proposed a distributed rewiring model that creates networks with features similar to those found in complex networks. Although each node acts in a distributed way and seeking to reduce only the trajectories of its packets, we observed a decrease of diameter and an increase in clustering coefficient in the global structure compared to the initial graph. Furthermore, we can find different final structures depending on slight changes in the local rewiring rules.

Feedforward Categorization on AER Motion Events Using Cortex-Like Features in a Spiking Neural Network.

PubMed

Zhao, Bo; Ding, Ruoxi; Chen, Shoushun; Linares-Barranco, Bernabe; Tang, Huajin

2015-09-01

This paper introduces an event-driven feedforward categorization system, which takes data from a temporal contrast address event representation (AER) sensor. The proposed system extracts bio-inspired cortex-like features and discriminates different patterns using an AER based tempotron classifier (a network of leaky integrate-and-fire spiking neurons). One of the system's most appealing characteristics is its event-driven processing, with both input and features taking the form of address events (spikes). The system was evaluated on an AER posture dataset and compared with two recently developed bio-inspired models. Experimental results have shown that it consumes much less simulation time while still maintaining comparable performance. In addition, experiments on the Mixed National Institute of Standards and Technology (MNIST) image dataset have demonstrated that the proposed system can work not only on raw AER data but also on images (with a preprocessing step to convert images into AER events) and that it can maintain competitive accuracy even when noise is added. The system was further evaluated on the MNIST dynamic vision sensor dataset (in which data is recorded using an AER dynamic vision sensor), with testing accuracy of 88.14%.

Applying cybernetic technology to diagnose human pulmonary sounds.

PubMed

Chen, Mei-Yung; Chou, Cheng-Han

2014-06-01

Chest auscultation is a crucial and efficient method for diagnosing lung disease; however, it is a subjective process that relies on physician experience and the ability to differentiate between various sound patterns. Because the physiological signals composed of heart sounds and pulmonary sounds (PSs) are greater than 120 Hz and the human ear is not sensitive to low frequencies, successfully making diagnostic classifications is difficult. To solve this problem, we constructed various PS recognition systems for classifying six PS classes: vesicular breath sounds, bronchial breath sounds, tracheal breath sounds, crackles, wheezes, and stridor sounds. First, we used a piezoelectric microphone and data acquisition card to acquire PS signals and perform signal preprocessing. A wavelet transform was used for feature extraction, and the PS signals were decomposed into frequency subbands. Using a statistical method, we extracted 17 features that were used as the input vectors of a neural network. We proposed a 2-stage classifier combined with a back-propagation (BP) neural network and learning vector quantization (LVQ) neural network, which improves classification accuracy by using a haploid neural network. The receiver operating characteristic (ROC) curve verifies the high performance level of the neural network. To expand traditional auscultation methods, we constructed various PS diagnostic systems that can correctly classify the six common PSs. The proposed device overcomes the lack of human sensitivity to low-frequency sounds and various PS waves, characteristic values, and a spectral analysis charts are provided to elucidate the design of the human-machine interface.

Additional Security Considerations for Grid Management

NASA Technical Reports Server (NTRS)

Eidson, Thomas M.

2003-01-01

The use of Grid computing environments is growing in popularity. A Grid computing environment is primarily a wide area network that encompasses multiple local area networks, where some of the local area networks are managed by different organizations. A Grid computing environment also includes common interfaces for distributed computing software so that the heterogeneous set of machines that make up the Grid can be used more easily. The other key feature of a Grid is that the distributed computing software includes appropriate security technology. The focus of most Grid software is on the security involved with application execution, file transfers, and other remote computing procedures. However, there are other important security issues related to the management of a Grid and the users who use that Grid. This note discusses these additional security issues and makes several suggestions as how they can be managed.

Enhancing LoRaWAN Security through a Lightweight and Authenticated Key Management Approach.

PubMed

Sanchez-Iborra, Ramon; Sánchez-Gómez, Jesús; Pérez, Salvador; Fernández, Pedro J; Santa, José; Hernández-Ramos, José L; Skarmeta, Antonio F

2018-06-05

Luckily, new communication technologies and protocols are nowadays designed considering security issues. A clear example of this can be found in the Internet of Things (IoT) field, a quite recent area where communication technologies such as ZigBee or IPv6 over Low power Wireless Personal Area Networks (6LoWPAN) already include security features to guarantee authentication, confidentiality and integrity. More recent technologies are Low-Power Wide-Area Networks (LP-WAN), which also consider security, but present initial approaches that can be further improved. An example of this can be found in Long Range (LoRa) and its layer-two supporter LoRa Wide Area Network (LoRaWAN), which include a security scheme based on pre-shared cryptographic material lacking flexibility when a key update is necessary. Because of this, in this work, we evaluate the security vulnerabilities of LoRaWAN in the area of key management and propose different alternative schemes. Concretely, the application of an approach based on the recently specified Ephemeral Diffie⁻Hellman Over COSE (EDHOC) is found as a convenient solution, given its flexibility in the update of session keys, its low computational cost and the limited message exchanges needed. A comparative conceptual analysis considering the overhead of different security schemes for LoRaWAN is carried out in order to evaluate their benefits in the challenging area of LP-WAN.

Applications of statistical physics to technology price evolution

NASA Astrophysics Data System (ADS)

McNerney, James

Understanding how changing technology affects the prices of goods is a problem with both rich phenomenology and important policy consequences. Using methods from statistical physics, I model technology-driven price evolution. First, I examine a model for the price evolution of individual technologies. The price of a good often follows a power law equation when plotted against its cumulative production. This observation turns out to have significant consequences for technology policy aimed at mitigating climate change, where technologies are needed that achieve low carbon emissions at low cost. However, no theory adequately explains why technology prices follow power laws. To understand this behavior, I simplify an existing model that treats technologies as machines composed of interacting components. I find that the power law exponent of the price trajectory is inversely related to the number of interactions per component. I extend the model to allow for more realistic component interactions and make a testable prediction. Next, I conduct a case-study on the cost evolution of coal-fired electricity. I derive the cost in terms of various physical and economic components. The results suggest that commodities and technologies fall into distinct classes of price models, with commodities following martingales, and technologies following exponentials in time or power laws in cumulative production. I then examine the network of money flows between industries. This work is a precursor to studying the simultaneous evolution of multiple technologies. Economies resemble large machines, with different industries acting as interacting components with specialized functions. To begin studying the structure of these machines, I examine 20 economies with an emphasis on finding common features to serve as targets for statistical physics models. I find they share the same money flow and industry size distributions. I apply methods from statistical physics to show that industries cluster the same way according to industry type. Finally, I use these industry money flows to model the price evolution of many goods simultaneously, where network effects become important. I derive a prediction for which goods tend to improve most rapidly. The fastest-improving goods are those with the highest mean path lengths in the money flow network.

Categorical Structure among Shared Features in Networks of Early-Learned Nouns

ERIC Educational Resources Information Center

Hills, Thomas T.; Maouene, Mounir; Maouene, Josita; Sheya, Adam; Smith, Linda

2009-01-01

The shared features that characterize the noun categories that young children learn first are a formative basis of the human category system. To investigate the potential categorical information contained in the features of early-learned nouns, we examine the graph-theoretic properties of noun-feature networks. The networks are built from the…

Intervertebral disc detection in X-ray images using faster R-CNN.

PubMed

Ruhan Sa; Owens, William; Wiegand, Raymond; Studin, Mark; Capoferri, Donald; Barooha, Kenneth; Greaux, Alexander; Rattray, Robert; Hutton, Adam; Cintineo, John; Chaudhary, Vipin

2017-07-01

Automatic identification of specific osseous landmarks on the spinal radiograph can be used to automate calculations for correcting ligament instability and injury, which affect 75% of patients injured in motor vehicle accidents. In this work, we propose to use deep learning based object detection method as the first step towards identifying landmark points in lateral lumbar X-ray images. The significant breakthrough of deep learning technology has made it a prevailing choice for perception based applications, however, the lack of large annotated training dataset has brought challenges to utilizing the technology in medical image processing field. In this work, we propose to fine tune a deep network, Faster-RCNN, a state-of-the-art deep detection network in natural image domain, using small annotated clinical datasets. In the experiment we show that, by using only 81 lateral lumbar X-Ray training images, one can achieve much better performance compared to traditional sliding window detection method on hand crafted features. Furthermore, we fine-tuned the network using 974 training images and tested on 108 images, which achieved average precision of 0.905 with average computation time of 3 second per image, which greatly outperformed traditional methods in terms of accuracy and efficiency.

Lung assist device technology with physiologic blood flow developed on a tissue engineered scaffold platform.

PubMed

Hoganson, David M; Pryor, Howard I; Bassett, Erik K; Spool, Ira D; Vacanti, Joseph P

2011-02-21

There is no technology available to support failing lung function for patients outside the hospital. An implantable lung assist device would augment lung function as a bridge to transplant or possible destination therapy. Utilizing biomimetic design principles, a microfluidic vascular network was developed for blood inflow from the pulmonary artery and blood return to the left atrium. Computational fluid dynamics analysis was used to optimize blood flow within the vascular network. A micro milled variable depth mold with 3D features was created to achieve both physiologic blood flow and shear stress. Gas exchange occurs across a thin silicone membrane between the vascular network and adjacent alveolar chamber with flowing oxygen. The device had a surface area of 23.1 cm(2) and respiratory membrane thickness of 8.7 ± 1.2 μm. Carbon dioxide transfer within the device was 156 ml min(-1) m(-2) and the oxygen transfer was 34 ml min(-1) m(-2). A lung assist device based on tissue engineering architecture achieves gas exchange comparable to hollow fiber oxygenators yet does so while maintaining physiologic blood flow. This device may be scaled up to create an implantable ambulatory lung assist device.

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System.

PubMed

Sheik, Sadique; Coath, Martin; Indiveri, Giacomo; Denham, Susan L; Wennekers, Thomas; Chicca, Elisabetta

2012-01-01

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems.

Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System

PubMed Central

Sheik, Sadique; Coath, Martin; Indiveri, Giacomo; Denham, Susan L.; Wennekers, Thomas; Chicca, Elisabetta

2011-01-01

Many sounds of ecological importance, such as communication calls, are characterized by time-varying spectra. However, most neuromorphic auditory models to date have focused on distinguishing mainly static patterns, under the assumption that dynamic patterns can be learned as sequences of static ones. In contrast, the emergence of dynamic feature sensitivity through exposure to formative stimuli has been recently modeled in a network of spiking neurons based on the thalamo-cortical architecture. The proposed network models the effect of lateral and recurrent connections between cortical layers, distance-dependent axonal transmission delays, and learning in the form of Spike Timing Dependent Plasticity (STDP), which effects stimulus-driven changes in the pattern of network connectivity. In this paper we demonstrate how these principles can be efficiently implemented in neuromorphic hardware. In doing so we address two principle problems in the design of neuromorphic systems: real-time event-based asynchronous communication in multi-chip systems, and the realization in hybrid analog/digital VLSI technology of neural computational principles that we propose underlie plasticity in neural processing of dynamic stimuli. The result is a hardware neural network that learns in real-time and shows preferential responses, after exposure, to stimuli exhibiting particular spectro-temporal patterns. The availability of hardware on which the model can be implemented, makes this a significant step toward the development of adaptive, neurobiologically plausible, spike-based, artificial sensory systems. PMID:22347163

A new measure based on degree distribution that links information theory and network graph analysis

PubMed Central

2012-01-01

Background Detailed connection maps of human and nonhuman brains are being generated with new technologies, and graph metrics have been instrumental in understanding the general organizational features of these structures. Neural networks appear to have small world properties: they have clustered regions, while maintaining integrative features such as short average pathlengths. Results We captured the structural characteristics of clustered networks with short average pathlengths through our own variable, System Difference (SD), which is computationally simple and calculable for larger graph systems. SD is a Jaccardian measure generated by averaging all of the differences in the connection patterns between any two nodes of a system. We calculated SD over large random samples of matrices and found that high SD matrices have a low average pathlength and a larger number of clustered structures. SD is a measure of degree distribution with high SD matrices maximizing entropic properties. Phi (Φ), an information theory metric that assesses a system’s capacity to integrate information, correlated well with SD - with SD explaining over 90% of the variance in systems above 11 nodes (tested for 4 to 13 nodes). However, newer versions of Φ do not correlate well with the SD metric. Conclusions The new network measure, SD, provides a link between high entropic structures and degree distributions as related to small world properties. PMID:22726594

Research and Development of Hepatitis B Drugs: An Analysis Based on Technology Flows Measured by Patent Citations

PubMed Central

Wan, Jian-bo; He, Chengwei; Hu, Yuanjia

2016-01-01

Despite the existence of available therapies, the Hepatitis B virus infection continues to be one of the most serious threats to human health, especially in developing countries such as China and India. To shed light on the improvement of current therapies and development of novel anti-HBV drugs, we thoroughly investigated 212 US patents of anti-HBV drugs and analyzed the technology flow in research and development of anti-HBV drugs based on data from IMS LifeCycle databases. Moreover, utilizing the patent citation method, which is an effective indicator of technology flow, we constructed patent citation network models and performed network analysis in order to reveal the features of different technology clusters. As a result, we identified the stagnant status of anti-HBV drug development and pointed the way for development of domestic pharmaceuticals in developing countries. We also discussed about therapeutic vaccines as the potential next generation therapy for HBV infection. Lastly, we depicted the cooperation between entities and found that novel forms of cooperation added diversity to the conventional form of cooperation within the pharmaceutical industry. In summary, our study provides inspiring insights for investors, policy makers, researchers, and other readers interested in anti-HBV drug development. PMID:27727319

Salient object detection based on multi-scale contrast.

PubMed

Wang, Hai; Dai, Lei; Cai, Yingfeng; Sun, Xiaoqiang; Chen, Long

2018-05-01

Due to the development of deep learning networks, a salient object detection based on deep learning networks, which are used to extract the features, has made a great breakthrough compared to the traditional methods. At present, the salient object detection mainly relies on very deep convolutional network, which is used to extract the features. In deep learning networks, an dramatic increase of network depth may cause more training errors instead. In this paper, we use the residual network to increase network depth and to mitigate the errors caused by depth increase simultaneously. Inspired by image simplification, we use color and texture features to obtain simplified image with multiple scales by means of region assimilation on the basis of super-pixels in order to reduce the complexity of images and to improve the accuracy of salient target detection. We refine the feature on pixel level by the multi-scale feature correction method to avoid the feature error when the image is simplified at the above-mentioned region level. The final full connection layer not only integrates features of multi-scale and multi-level but also works as classifier of salient targets. The experimental results show that proposed model achieves better results than other salient object detection models based on original deep learning networks. Copyright © 2018 Elsevier Ltd. All rights reserved.

MDD diagnosis based on partial-brain functional connection network

NASA Astrophysics Data System (ADS)

Yan, Gaoliang; Hu, Hailong; Zhao, Xiang; Zhang, Lin; Qu, Zehui; Li, Yantao

2018-04-01

Artificial intelligence (AI) is a hotspot in computer science research nowadays. To apply AI technology in all industries has been the developing direction for researchers. Major depressive disorder (MDD) is a common disease of serious mental disorders. The World Health Organization (WHO) reports that MDD is projected to become the second most common cause of death and disability by 2020. At present, the way of MDD diagnosis is single. Applying AI technology to MDD diagnosis and pathophysiological research will speed up the MDD research and improve the efficiency of MDD diagnosis. In this study, we select the higher degree of brain network functional connectivity by statistical methods. And our experiments show that the average accuracy of Logistic Regression (LR) classifier using feature filtering reaches 88.48%. Compared with other classification methods, both the efficiency and accuracy of this method are improved, which will greatly improve the process of MDD diagnose. In these experiments, we also define the brain regions associated with MDD, which plays a vital role in MDD pathophysiological research.

RapidIO as a multi-purpose interconnect

NASA Astrophysics Data System (ADS)

Baymani, Simaolhoda; Alexopoulos, Konstantinos; Valat, Sébastien

2017-10-01

RapidIO (http://rapidio.org/) technology is a packet-switched high-performance fabric, which has been under active development since 1997. Originally meant to be a front side bus, it developed into a system level interconnect which is today used in all 4G/LTE base stations world wide. RapidIO is often used in embedded systems that require high reliability, low latency and scalability in a heterogeneous environment - features that are highly interesting for several use cases, such as data analytics and data acquisition (DAQ) networks. We will present the results of evaluating RapidIO in a data analytics environment, from setup to benchmark. Specifically, we will share the experience of running ROOT and Hadoop on top of RapidIO. To demonstrate the multi-purpose characteristics of RapidIO, we will also present the results of investigating RapidIO as a technology for high-speed DAQ networks using a generic multi-protocol event-building emulation tool. In addition we will present lessons learned from implementing native ports of CERN applications to RapidIO.

Configurable analog-digital conversion using the neural engineering framework

PubMed Central

Mayr, Christian G.; Partzsch, Johannes; Noack, Marko; Schüffny, Rene

2014-01-01

Efficient Analog-Digital Converters (ADC) are one of the mainstays of mixed-signal integrated circuit design. Besides the conventional ADCs used in mainstream ICs, there have been various attempts in the past to utilize neuromorphic networks to accomplish an efficient crossing between analog and digital domains, i.e., to build neurally inspired ADCs. Generally, these have suffered from the same problems as conventional ADCs, that is they require high-precision, handcrafted analog circuits and are thus not technology portable. In this paper, we present an ADC based on the Neural Engineering Framework (NEF). It carries out a large fraction of the overall ADC process in the digital domain, i.e., it is easily portable across technologies. The analog-digital conversion takes full advantage of the high degree of parallelism inherent in neuromorphic networks, making for a very scalable ADC. In addition, it has a number of features not commonly found in conventional ADCs, such as a runtime reconfigurability of the ADC sampling rate, resolution and transfer characteristic. PMID:25100933

Electronic Patient Reported Outcomes in Paediatric Oncology - Applying Mobile and Near Field Communication Technology.

PubMed

Duregger, Katharina; Hayn, Dieter; Nitzlnader, Michael; Kropf, Martin; Falgenhauer, Markus; Ladenstein, Ruth; Schreier, Günter

2016-01-01

Electronic Patient Reported Outcomes (ePRO) gathered using telemonitoring solutions might be a valuable source of information in rare cancer research. The objective of this paper was to develop a concept and implement a prototype for introducing ePRO into the existing neuroblastoma research network by applying Near Field Communication and mobile technology. For physicians, an application was developed for registering patients within the research network and providing patients with an ID card and a PIN for authentication when transmitting telemonitoring data to the Electronic Data Capture system OpenClinica. For patients, a previously developed telemonitoring system was extended by a Simple Object Access Protocol (SOAP) interface for transmitting nine different health parameters and toxicities. The concept was fully implemented on the front-end side. The developed application for physicians was prototypically implemented and the mobile application of the telemonitoring system was successfully connected to OpenClinica. Future work will focus on the implementation of the back-end features.

The Usefulness of Information and Communication Technologies in Crisis Response

PubMed Central

Paul, Sharoda A.; Reddy, Madhu; Abraham, Joanna; DeFlitch, Christopher

2008-01-01

Information and communication technologies (ICTs) play a vital role in coordinating crisis response between pre-hospital services and emergency departments of hospitals. In spite of the advances in these technologies, there remain a variety of challenges to their usage during a crisis. To identify these challenges, we conducted focus group interviews with emergency department (ED) and emergency medical services (EMS) personnel. We found that ED and EMS personnel have widely varying perceptions about the usefulness and ease-of-use of information tools and communication tools used in crisis management. We discuss the importance of bringing together communication and information tools into integrated networks of ICTs for effective crisis response. We also highlight design features of ICTs which can support seamless and effective communication and coordination between ED and EMS teams. PMID:18998898

The usefulness of information and communication technologies in crisis response.

PubMed

Paul, Sharoda A; Reddy, Madhu; Abraham, Joanna; DeFlitch, Christopher; Deflitch, Christopher J

2008-11-06

Information and communication technologies (ICTs) play a vital role in coordinating crisis response between pre-hospital services and emergency departments of hospitals. In spite of the advances in these technologies, there remain a variety of challenges to their usage during a crisis. To identify these challenges, we conducted focus group interviews with emergency department (ED) and emergency medical services (EMS) personnel. We found that ED and EMS personnel have widely varying perceptions about the usefulness and ease-of-use of information tools and communication tools used in crisis management. We discuss the importance of bringing together communication and information tools into integrated networks of ICTs for effective crisis response. We also highlight design features of ICTs which can support seamless and effective communication and coordination between ED and EMS teams.

SigFlux: a novel network feature to evaluate the importance of proteins in signal transduction networks.

PubMed

Liu, Wei; Li, Dong; Zhang, Jiyang; Zhu, Yunping; He, Fuchu

2006-11-27

Measuring each protein's importance in signaling networks helps to identify the crucial proteins in a cellular process, find the fragile portion of the biology system and further assist for disease therapy. However, there are relatively few methods to evaluate the importance of proteins in signaling networks. We developed a novel network feature to evaluate the importance of proteins in signal transduction networks, that we call SigFlux, based on the concept of minimal path sets (MPSs). An MPS is a minimal set of nodes that can perform the signal propagation from ligands to target genes or feedback loops. We define SigFlux as the number of MPSs in which each protein is involved. We applied this network feature to the large signal transduction network in the hippocampal CA1 neuron of mice. Significant correlations were simultaneously observed between SigFlux and both the essentiality and evolutionary rate of genes. Compared with another commonly used network feature, connectivity, SigFlux has similar or better ability as connectivity to reflect a protein's essentiality. Further classification according to protein function demonstrates that high SigFlux, low connectivity proteins are abundant in receptors and transcriptional factors, indicating that SigFlux candescribe the importance of proteins within the context of the entire network. SigFlux is a useful network feature in signal transduction networks that allows the prediction of the essentiality and conservation of proteins. With this novel network feature, proteins that participate in more pathways or feedback loops within a signaling network are proved far more likely to be essential and conserved during evolution than their counterparts.

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing

PubMed Central

Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

2016-01-01

Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery. PMID:27711246

Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

PubMed

Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

2016-01-01

Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

Evaluating open-source cloud computing solutions for geosciences

NASA Astrophysics Data System (ADS)

Huang, Qunying; Yang, Chaowei; Liu, Kai; Xia, Jizhe; Xu, Chen; Li, Jing; Gui, Zhipeng; Sun, Min; Li, Zhenglong

2013-09-01

Many organizations start to adopt cloud computing for better utilizing computing resources by taking advantage of its scalability, cost reduction, and easy to access characteristics. Many private or community cloud computing platforms are being built using open-source cloud solutions. However, little has been done to systematically compare and evaluate the features and performance of open-source solutions in supporting Geosciences. This paper provides a comprehensive study of three open-source cloud solutions, including OpenNebula, Eucalyptus, and CloudStack. We compared a variety of features, capabilities, technologies and performances including: (1) general features and supported services for cloud resource creation and management, (2) advanced capabilities for networking and security, and (3) the performance of the cloud solutions in provisioning and operating the cloud resources as well as the performance of virtual machines initiated and managed by the cloud solutions in supporting selected geoscience applications. Our study found that: (1) no significant performance differences in central processing unit (CPU), memory and I/O of virtual machines created and managed by different solutions, (2) OpenNebula has the fastest internal network while both Eucalyptus and CloudStack have better virtual machine isolation and security strategies, (3) Cloudstack has the fastest operations in handling virtual machines, images, snapshots, volumes and networking, followed by OpenNebula, and (4) the selected cloud computing solutions are capable for supporting concurrent intensive web applications, computing intensive applications, and small-scale model simulations without intensive data communication.

Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture.

PubMed

Chen, C L Philip; Liu, Zhulin

2018-01-01

Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is established in the form of a flat network, where the original inputs are transferred and placed as "mapped features" in feature nodes and the structure is expanded in wide sense in the "enhancement nodes." The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded. Two incremental learning algorithms are given for both the increment of the feature nodes (or filters in deep structure) and the increment of the enhancement nodes. The designed model and algorithms are very versatile for selecting a model rapidly. In addition, another incremental learning is developed for a system that has been modeled encounters a new incoming input. Specifically, the system can be remodeled in an incremental way without the entire retraining from the beginning. Satisfactory result for model reduction using singular value decomposition is conducted to simplify the final structure. Compared with existing deep neural networks, experimental results on the Modified National Institute of Standards and Technology database and NYU NORB object recognition dataset benchmark data demonstrate the effectiveness of the proposed BLS.

A VGI data integration framework based on linked data model

NASA Astrophysics Data System (ADS)

Wan, Lin; Ren, Rongrong

2015-12-01

This paper aims at the geographic data integration and sharing method for multiple online VGI data sets. We propose a semantic-enabled framework for online VGI sources cooperative application environment to solve a target class of geospatial problems. Based on linked data technologies - which is one of core components of semantic web, we can construct the relationship link among geographic features distributed in diverse VGI platform by using linked data modeling methods, then deploy these semantic-enabled entities on the web, and eventually form an interconnected geographic data network to support geospatial information cooperative application across multiple VGI data sources. The mapping and transformation from VGI sources to RDF linked data model is presented to guarantee the unique data represent model among different online social geographic data sources. We propose a mixed strategy which combined spatial distance similarity and feature name attribute similarity as the measure standard to compare and match different geographic features in various VGI data sets. And our work focuses on how to apply Markov logic networks to achieve interlinks of the same linked data in different VGI-based linked data sets. In our method, the automatic generating method of co-reference object identification model according to geographic linked data is discussed in more detail. It finally built a huge geographic linked data network across loosely-coupled VGI web sites. The results of the experiment built on our framework and the evaluation of our method shows the framework is reasonable and practicable.

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

PubMed

Hagen, Espen; Dahmen, David; Stavrinou, Maria L; Lindén, Henrik; Tetzlaff, Tom; van Albada, Sacha J; Grün, Sonja; Diesmann, Markus; Einevoll, Gaute T

2016-12-01

With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm 2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail. © The Author 2016. Published by Oxford University Press.

Experimental high-speed network

NASA Astrophysics Data System (ADS)

McNeill, Kevin M.; Klein, William P.; Vercillo, Richard; Alsafadi, Yasser H.; Parra, Miguel V.; Dallas, William J.

1993-09-01

Many existing local area networking protocols currently applied in medical imaging were originally designed for relatively low-speed, low-volume networking. These protocols utilize small packet sizes appropriate for text based communication. Local area networks of this type typically provide raw bandwidth under 125 MHz. These older network technologies are not optimized for the low delay, high data traffic environment of a totally digital radiology department. Some current implementations use point-to-point links when greater bandwidth is required. However, the use of point-to-point communications for a total digital radiology department network presents many disadvantages. This paper describes work on an experimental multi-access local area network called XFT. The work includes the protocol specification, and the design and implementation of network interface hardware and software. The protocol specifies the Physical and Data Link layers (OSI layers 1 & 2) for a fiber-optic based token ring providing a raw bandwidth of 500 MHz. The protocol design and implementation of the XFT interface hardware includes many features to optimize image transfer and provide flexibility for additional future enhancements which include: a modular hardware design supporting easy portability to a variety of host system buses, a versatile message buffer design providing 16 MB of memory, and the capability to extend the raw bandwidth of the network to 3.0 GHz.

Cost- and reliability-oriented aggregation point association in long-term evolution and passive optical network hybrid access infrastructure for smart grid neighborhood area network

NASA Astrophysics Data System (ADS)

Cheng, Xiao; Feng, Lei; Zhou, Fanqin; Wei, Lei; Yu, Peng; Li, Wenjing

2018-02-01

With the rapid development of the smart grid, the data aggregation point (AP) in the neighborhood area network (NAN) is becoming increasingly important for forwarding the information between the home area network and wide area network. Due to limited budget, it is unable to use one-single access technology to meet the ongoing requirements on AP coverage. This paper first introduces the wired and wireless hybrid access network with the integration of long-term evolution (LTE) and passive optical network (PON) system for NAN, which allows a good trade-off among cost, flexibility, and reliability. Then, based on the already existing wireless LTE network, an AP association optimization model is proposed to make the PON serve as many APs as possible, considering both the economic efficiency and network reliability. Moreover, since the features of the constraints and variables of this NP-hard problem, a hybrid intelligent optimization algorithm is proposed, which is achieved by the mixture of the genetic, ant colony and dynamic greedy algorithm. By comparing with other published methods, simulation results verify the performance of the proposed method in improving the AP coverage and the performance of the proposed algorithm in terms of convergence.

Making Better Use of Bandwidth: Data Compression and Network Management Technologies

DTIC Science & Technology

2005-01-01

data , the compression would not be a success. A key feature of the Lempel - Ziv family of algorithms is that the...citeseer.nj.nec.com/yu02motion.html. Ziv , J., and A. Lempel , “A Universal Algorithm for Sequential Data Compression ,” IEEE Transac- tions on Information Theory, Vol. 23, 1977, pp. 337–342. ...probability models – Lempel - Ziv – Prediction by partial matching The central component of a lossless compression algorithm

Dynamic subframe allocation for mobile broadband m-health using IEEE 802.16j mobile multihop relay networks.

PubMed

Alinejad, Ali; Istepanian, R S H; Philip, N

2012-01-01

The concept of 4G health will be one of the key focus areas of future m-health research and enterprise activities in the coming years. WiMAX technology is one of the constituent 4G wireless technologies that provides broadband wireless access (BWA). Despite the fact that WiMAX is able to provide a high data rate in a relatively large coverage; this technology has specific limitations such as: coverage, signal attenuation problems due to shadowing or path loss, and limited available spectrum. The IEEE 802.16j mobile multihop relay (MMR) technology is a pragmatic solution designed to overcome these limitations. The aim of IEEE 802.16j MMR is to expand the IEEE 802.16e's capabilities with multihop features. In particular, the uplink (UL) and downlink (DL) subframe allocation in WiMAX network is usually fixed. However, dynamic frame allocation is a useful mechanism to optimize uplink and downlink subframe size dynamically based on the traffic conditions through real-time traffic monitoring. This particular mechanism is important for future WiMAX based m-health applications as it allows the tradeoff in both UL and DL channels. In this paper, we address the dynamic frame allocation issue in IEEE 802.16j MMR network for m-health applications. A comparative performance analysis of the proposed approach is validated using the OPNET Modeler(®). The simulation results have shown an improved performance of resource allocation and end-to-end delay performance for typical medical video streaming application.

Real-time object-to-features vectorisation via Siamese neural networks

NASA Astrophysics Data System (ADS)

Fedorenko, Fedor; Usilin, Sergey

2017-03-01

Object-to-features vectorisation is a hard problem to solve for objects that can be hard to distinguish. Siamese and Triplet neural networks are one of the more recent tools used for such task. However, most networks used are very deep networks that prove to be hard to compute in the Internet of Things setting. In this paper, a computationally efficient neural network is proposed for real-time object-to-features vectorisation into a Euclidean metric space. We use L2 distance to reflect feature vector similarity during both training and testing. In this way, feature vectors we develop can be easily classified using K-Nearest Neighbours classifier. Such approach can be used to train networks to vectorise such "problematic" objects like images of human faces, keypoint image patches, like keypoints on Arctic maps and surrounding marine areas.

Emergent technologies: 25 years

NASA Astrophysics Data System (ADS)

Rising, Hawley K.

2013-03-01

This paper will talk about the technologies that have been emerging over the 25 years since the Human Vision and Electronic Imaging conference began that the conference has been a part of, and that have been a part of the conference, and will look at those technologies that are emerging today, such as social networks, haptic technologies, and still emerging imaging technologies, and what we might look at for the future.Twenty-five years is a long time, and it is not without difficulty that we remember what was emerging in the late 1980s. Yet to be developed: The first commercial digital still camera was not yet on the market, although there were hand held electronic cameras. Personal computers were not displaying standardized images, and image quality was not something that could be talked about in a standardized fashion, if only because image compression algorithms were not standardized yet for several years hence. Even further away were any standards for movie compression standards, there was no personal computer even on the horizon which could display them. What became an emergent technology and filled many sessions later, image comparison and search, was not possible, nor the current emerging technology of social networks- the world wide web was still several years away. Printer technology was still devising dithers and image size manipulations which would consume many years, as would scanning technology, and image quality for both was a major issue for dithers and Fourier noise.From these humble beginnings to the current moves that are changing computing and the meaning of both electronic devices and human interaction with them, we will see a course through the changing technology that holds some features constant for many years, while others come and go.

Visual feature extraction and establishment of visual tags in the intelligent visual internet of things

NASA Astrophysics Data System (ADS)

Zhao, Yiqun; Wang, Zhihui

2015-12-01

The Internet of things (IOT) is a kind of intelligent networks which can be used to locate, track, identify and supervise people and objects. One of important core technologies of intelligent visual internet of things ( IVIOT) is the intelligent visual tag system. In this paper, a research is done into visual feature extraction and establishment of visual tags of the human face based on ORL face database. Firstly, we use the principal component analysis (PCA) algorithm for face feature extraction, then adopt the support vector machine (SVM) for classifying and face recognition, finally establish a visual tag for face which is already classified. We conducted a experiment focused on a group of people face images, the result show that the proposed algorithm have good performance, and can show the visual tag of objects conveniently.

Cost-effective FITL technologies for small business and residential customers

NASA Astrophysics Data System (ADS)

Andersen, Niels E.; Woolnough, Peter; Seidenberg, Juergen; Ferreira, Mario F. S.

1995-02-01

FIRST is a RACE project where 5 main European telecoms operators, 4 equipment manufacturers and one university have joined up to define and test in a field trial in Portugal a cost effective Optical Access Network. The main design target has been a system which gives cost effective provision of wideband services for small and medium business customers. The system however, incorporates provision of telephone, ISDN and analog and digital video for residential customers as well. Technologies have been chosen with the objective of providing a simple, robust and flexible system where initial deployment costs are low and closely related to the service take up. The paper describes the main technical features of the system and network applications which shows how the system may be introduced in network planning. The system is based on Passive Optical Network technology where video is distributed in the 1550 nm window and telecoms services transmitted at 1300 nm in full duplex mode. The telecoms system provides high capacity, flexibility in loop length and robustness towards outside plant performance. The Subcarrier Multiple Access (SCMA) method is used for upstream transmission of bi-directional telecoms services. SCMA has advantages compared to the Time Division Multiple Access technology used in other systems. Bandwidth/cost tradeoff is better and the lower requirements to the outside plant increases the overall cost benefit. Optical beat noise due to overlapping of laser spectra which may be a problem for this technology has been addressed with success through the use of a suitable modulation and control technique. This technology is further validated in the field trial. The video system provides cost effective long distance transmission on standard fiber with externally modulated lasers and cascaded amplifiers. Coexistence of analog and digital video on one fiber with different modulation schemes i.e. BPSK, QPSK and 64 QAM have been validated. Total life cycle cost evaluations based on availability data, maintenance requirements and expectations for service development have been made. The field trial will be running for two years.

Classification of arrhythmia using hybrid networks.

PubMed

Haseena, Hassan H; Joseph, Paul K; Mathew, Abraham T

2011-12-01

Reliable detection of arrhythmias based on digital processing of Electrocardiogram (ECG) signals is vital in providing suitable and timely treatment to a cardiac patient. Due to corruption of ECG signals with multiple frequency noise and presence of multiple arrhythmic events in a cardiac rhythm, computerized interpretation of abnormal ECG rhythms is a challenging task. This paper focuses a Fuzzy C- Mean (FCM) clustered Probabilistic Neural Network (PNN) and Multi Layered Feed Forward Network (MLFFN) for the discrimination of eight types of ECG beats. Parameters such as fourth order Auto Regressive (AR) coefficients along with Spectral Entropy (SE) are extracted from each ECG beat and feature reduction has been carried out using FCM clustering. The cluster centers form the input of neural network classifiers. The extensive analysis of Massachusetts Institute of Technology- Beth Israel Hospital (MIT-BIH) arrhythmia database shows that FCM clustered PNNs is superior in cardiac arrhythmia classification than FCM clustered MLFFN with an overall accuracy of 99.05%, 97.14%, respectively.

An assessment of technology-based service encounters & network security on the e-health care systems of medical centers in Taiwan

PubMed Central

Chang, Hsin Hsin; Chang, Ching Sheng

2008-01-01

Background Enhancing service efficiency and quality has always been one of the most important factors to heighten competitiveness in the health care service industry. Thus, how to utilize information technology to reduce work load for staff and expeditiously improve work efficiency and healthcare service quality is presently the top priority for every healthcare institution. In this fast changing modern society, e-health care systems are currently the best possible way to achieve enhanced service efficiency and quality under the restraint of healthcare cost control. The electronic medical record system and the online appointment system are the core features in employing e-health care systems in the technology-based service encounters. Methods This study implemented the Service Encounters Evaluation Model, the European Customer Satisfaction Index, the Attribute Model and the Overall Affect Model for model inference. A total of 700 copies of questionnaires from two authoritative southern Taiwan medical centers providing the electronic medical record system and the online appointment system service were distributed, among which 590 valid copies were retrieved with a response rate of 84.3%. We then used SPSS 11.0 and the Linear Structural Relationship Model (LISREL 8.54) to analyze and evaluate the data. Results The findings are as follows: (1) Technology-based service encounters have a positive impact on service quality, but not patient satisfaction; (2) After experiencing technology-based service encounters, the cognition of the service quality has a positive effect on patient satisfaction; and (3) Network security contributes a positive moderating effect on service quality and patient satisfaction. Conclusion It revealed that the impact of electronic workflow (online appointment system service) on service quality was greater than electronic facilities (electronic medical record systems) in technology-based service encounters. Convenience and credibility are the most important factors of service quality in technology-based service encounters that patients demand. Due to the openness of networks, patients worry that transaction information could be intercepted; also, the credibility of the hospital involved is even a bigger concern, as patients have a strong sense of distrust. Therefore, in the operation of technology-based service encounters, along with providing network security, it is essential to build an atmosphere of psychological trust. PMID:18419820

An assessment of technology-based service encounters & network security on the e-health care systems of medical centers in Taiwan.

PubMed

Chang, Hsin Hsin; Chang, Ching Sheng

2008-04-17

Enhancing service efficiency and quality has always been one of the most important factors to heighten competitiveness in the health care service industry. Thus, how to utilize information technology to reduce work load for staff and expeditiously improve work efficiency and healthcare service quality is presently the top priority for every healthcare institution. In this fast changing modern society, e-health care systems are currently the best possible way to achieve enhanced service efficiency and quality under the restraint of healthcare cost control. The electronic medical record system and the online appointment system are the core features in employing e-health care systems in the technology-based service encounters. This study implemented the Service Encounters Evaluation Model, the European Customer Satisfaction Index, the Attribute Model and the Overall Affect Model for model inference. A total of 700 copies of questionnaires from two authoritative southern Taiwan medical centers providing the electronic medical record system and the online appointment system service were distributed, among which 590 valid copies were retrieved with a response rate of 84.3%. We then used SPSS 11.0 and the Linear Structural Relationship Model (LISREL 8.54) to analyze and evaluate the data. The findings are as follows: (1) Technology-based service encounters have a positive impact on service quality, but not patient satisfaction; (2) After experiencing technology-based service encounters, the cognition of the service quality has a positive effect on patient satisfaction; and (3) Network security contributes a positive moderating effect on service quality and patient satisfaction. It revealed that the impact of electronic workflow (online appointment system service) on service quality was greater than electronic facilities (electronic medical record systems) in technology-based service encounters. Convenience and credibility are the most important factors of service quality in technology-based service encounters that patients demand. Due to the openness of networks, patients worry that transaction information could be intercepted; also, the credibility of the hospital involved is even a bigger concern, as patients have a strong sense of distrust. Therefore, in the operation of technology-based service encounters, along with providing network security, it is essential to build an atmosphere of psychological trust.

node2vec: Scalable Feature Learning for Networks

PubMed Central

Grover, Aditya; Leskovec, Jure

2016-01-01

Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node’s network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks. PMID:27853626

Co-production in practice: how people with assisted living needs can help design and evolve technologies and services.

PubMed

Wherton, Joseph; Sugarhood, Paul; Procter, Rob; Hinder, Sue; Greenhalgh, Trisha

2015-05-26

The low uptake of telecare and telehealth services by older people may be explained by the limited involvement of users in the design. If the ambition of 'care closer to home' is to be realised, then industry, health and social care providers must evolve ways to work with older people to co-produce useful and useable solutions. We conducted 10 co-design workshops with users of telehealth and telecare, their carers, service providers and technology suppliers. Using vignettes developed from in-depth ethnographic case studies, we explored participants' perspectives on the design features of technologies and services to enable and facilitate the co-production of new care solutions. Workshop discussions were audio recorded, transcribed and analysed thematically. Analysis revealed four main themes. First, there is a need to raise awareness and provide information to potential users of assisted living technologies (ALTs). Second, technologies must be highly customisable and adaptable to accommodate the multiple and changing needs of different users. Third, the service must align closely with the individual's wider social support network. Finally, the service must support a high degree of information sharing and coordination. The case vignettes within inclusive and democratic co-design workshops provided a powerful means for ALT users and their carers to contribute, along with other stakeholders, to technology and service design. The workshops identified a need to focus attention on supporting the social processes that facilitate the collective efforts of formal and informal care networks in ALT delivery and use.

78 FR 17418 - Rural Health Information Technology Network Development Grant

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-21

... Information Technology Network Development Grant AGENCY: Health Resources and Services Administration (HRSA...-competitive replacement award under the Rural Health Information Technology Network Development Grant (RHITND... relinquishing its fiduciary responsibilities for the Rural Health Information Technology Network Development...

Compliance and Functional Testing of IEEE 1451.1 for NCAP-to-NCAP Communications in a Sensor Network

NASA Technical Reports Server (NTRS)

Figueroa, Jorge; Gurkan, Deniz; Yuan, X.; Benhaddou, D.; Liu, H.; Singla, A.; Franzl, R.; Ma, H.; Bhatt, S.; Morris, J.;

Maximum entropy methods for extracting the learned features of deep neural networks.

PubMed

Finnegan, Alex; Song, Jun S

2017-10-01

New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

Sentiment classification technology based on Markov logic networks

NASA Astrophysics Data System (ADS)

He, Hui; Li, Zhigang; Yao, Chongchong; Zhang, Weizhe

2016-07-01

With diverse online media emerging, there is a growing concern of sentiment classification problem. At present, text sentiment classification mainly utilizes supervised machine learning methods, which feature certain domain dependency. On the basis of Markov logic networks (MLNs), this study proposed a cross-domain multi-task text sentiment classification method rooted in transfer learning. Through many-to-one knowledge transfer, labeled text sentiment classification, knowledge was successfully transferred into other domains, and the precision of the sentiment classification analysis in the text tendency domain was improved. The experimental results revealed the following: (1) the model based on a MLN demonstrated higher precision than the single individual learning plan model. (2) Multi-task transfer learning based on Markov logical networks could acquire more knowledge than self-domain learning. The cross-domain text sentiment classification model could significantly improve the precision and efficiency of text sentiment classification.

Novel WRM-based architecture of hybrid PON featuring online access and full-fiber-fault protection for smart grid

NASA Astrophysics Data System (ADS)

Li, Xingfeng; Gan, Chaoqin; Liu, Zongkang; Yan, Yuqi; Qiao, HuBao

2018-01-01

In this paper, a novel architecture of hybrid PON for smart grid is proposed by introducing a wavelength-routing module (WRM). By using conventional optical passive components, a WRM with M ports is designed. The symmetry and passivity of the WRM makes it be easily integrated and very cheap in practice. Via the WRM, two types of network based on different ONU-interconnected manner can realize online access. Depending on optical switches and interconnecting fibers, full-fiber-fault protection and dynamic bandwidth allocation are realized in these networks. With the help of amplitude modulation, DPSK modulation and RSOA technology, wavelength triple-reuse is achieved. By means of injecting signals into left and right branches in access ring simultaneously, the transmission delay is decreased. Finally, the performance analysis and simulation of the network verifies the feasibility of the proposed architecture.

A new SMART sensing system for aerospace structures

NASA Astrophysics Data System (ADS)

Zhang, David C.; Yu, Pin; Beard, Shawn; Qing, Peter; Kumar, Amrita; Chang, Fu-Kuo

2007-04-01

It is essential to ensure the safety and reliability of in-service structures such as unmanned vehicles by detecting structural cracking, corrosion, delamination, material degradation and other types of damage in time. Utilization of an integrated sensor network system can enable automatic inspection of such damages ultimately. Using a built-in network of actuators and sensors, Acellent is providing tools for advanced structural diagnostics. Acellent's integrated structural health monitoring system consists of an actuator/sensor network, supporting signal generation and data acquisition hardware, and data processing, visualization and analysis software. This paper describes the various features of Acellent's latest SMART sensing system. The new system is USB-based and is ultra-portable using the state-of-the-art technology, while delivering many functions such as system self-diagnosis, sensor diagnosis, through-transmission mode and pulse-echo mode of operation and temperature measurement. Performance of the new system was evaluated for assessment of damage in composite structures.

Estimation of Dynamic Systems for Gene Regulatory Networks from Dependent Time-Course Data.

PubMed

Kim, Yoonji; Kim, Jaejik

2018-06-15

Dynamic system consisting of ordinary differential equations (ODEs) is a well-known tool for describing dynamic nature of gene regulatory networks (GRNs), and the dynamic features of GRNs are usually captured through time-course gene expression data. Owing to high-throughput technologies, time-course gene expression data have complex structures such as heteroscedasticity, correlations between genes, and time dependence. Since gene experiments typically yield highly noisy data with small sample size, for a more accurate prediction of the dynamics, the complex structures should be taken into account in ODE models. Hence, this study proposes an ODE model considering such data structures and a fast and stable estimation method for the ODE parameters based on the generalized profiling approach with data smoothing techniques. The proposed method also provides statistical inference for the ODE estimator and it is applied to a zebrafish retina cell network.

Cytoscape tools for the web age: D3.js and Cytoscape.js exporters

PubMed Central

Ono, Keiichiro; Demchak, Barry; Ideker, Trey

2014-01-01

In this paper we present new data export modules for Cytoscape 3 that can generate network files for Cytoscape.js and D3.js. Cytoscape.js exporter is implemented as a core feature of Cytoscape 3, and D3.js exporter is available as a Cytoscape 3 app. These modules enable users to seamlessly export network and table data sets generated in Cytoscape to popular JavaScript library readable formats. In addition, we implemented template web applications for browser-based interactive network visualization that can be used as basis for complex data visualization applications for bioinformatics research. Example web applications created with these tools demonstrate how Cytoscape works in modern data visualization workflows built with traditional desktop tools and emerging web-based technologies. This interactivity enables researchers more flexibility than with static images, thereby greatly improving the quality of insights researchers can gain from them. PMID:25520778

Cytoscape tools for the web age: D3.js and Cytoscape.js exporters.

PubMed

Ono, Keiichiro; Demchak, Barry; Ideker, Trey

2014-01-01

In this paper we present new data export modules for Cytoscape 3 that can generate network files for Cytoscape.js and D3.js. Cytoscape.js exporter is implemented as a core feature of Cytoscape 3, and D3.js exporter is available as a Cytoscape 3 app. These modules enable users to seamlessly export network and table data sets generated in Cytoscape to popular JavaScript library readable formats. In addition, we implemented template web applications for browser-based interactive network visualization that can be used as basis for complex data visualization applications for bioinformatics research. Example web applications created with these tools demonstrate how Cytoscape works in modern data visualization workflows built with traditional desktop tools and emerging web-based technologies. This interactivity enables researchers more flexibility than with static images, thereby greatly improving the quality of insights researchers can gain from them.

Crack orientation and depth estimation in a low-pressure turbine disc using a phased array ultrasonic transducer and an artificial neural network.

PubMed

Yang, Xiaoxia; Chen, Shili; Jin, Shijiu; Chang, Wenshuang

2013-09-13

Stress corrosion cracks (SCC) in low-pressure steam turbine discs are serious hidden dangers to production safety in the power plants, and knowing the orientation and depth of the initial cracks is essential for the evaluation of the crack growth rate, propagation direction and working life of the turbine disc. In this paper, a method based on phased array ultrasonic transducer and artificial neural network (ANN), is proposed to estimate both the depth and orientation of initial cracks in the turbine discs. Echo signals from cracks with different depths and orientations were collected by a phased array ultrasonic transducer, and the feature vectors were extracted by wavelet packet, fractal technology and peak amplitude methods. The radial basis function (RBF) neural network was investigated and used in this application. The final results demonstrated that the method presented was efficient in crack estimation tasks.

Crack Orientation and Depth Estimation in a Low-Pressure Turbine Disc Using a Phased Array Ultrasonic Transducer and an Artificial Neural Network

PubMed Central

Yang, Xiaoxia; Chen, Shili; Jin, Shijiu; Chang, Wenshuang

2013-01-01

Stress corrosion cracks (SCC) in low-pressure steam turbine discs are serious hidden dangers to production safety in the power plants, and knowing the orientation and depth of the initial cracks is essential for the evaluation of the crack growth rate, propagation direction and working life of the turbine disc. In this paper, a method based on phased array ultrasonic transducer and artificial neural network (ANN), is proposed to estimate both the depth and orientation of initial cracks in the turbine discs. Echo signals from cracks with different depths and orientations were collected by a phased array ultrasonic transducer, and the feature vectors were extracted by wavelet packet, fractal technology and peak amplitude methods. The radial basis function (RBF) neural network was investigated and used in this application. The final results demonstrated that the method presented was efficient in crack estimation tasks. PMID:24064602

CSP: A Multifaceted Hybrid Architecture for Space Computing

NASA Technical Reports Server (NTRS)

Rudolph, Dylan; Wilson, Christopher; Stewart, Jacob; Gauvin, Patrick; George, Alan; Lam, Herman; Crum, Gary Alex; Wirthlin, Mike; Wilson, Alex; Stoddard, Aaron

2014-01-01

Research on the CHREC Space Processor (CSP) takes a multifaceted hybrid approach to embedded space computing. Working closely with the NASA Goddard SpaceCube team, researchers at the National Science Foundation (NSF) Center for High-Performance Reconfigurable Computing (CHREC) at the University of Florida and Brigham Young University are developing hybrid space computers that feature an innovative combination of three technologies: commercial-off-the-shelf (COTS) devices, radiation-hardened (RadHard) devices, and fault-tolerant computing. Modern COTS processors provide the utmost in performance and energy-efficiency but are susceptible to ionizing radiation in space, whereas RadHard processors are virtually immune to this radiation but are more expensive, larger, less energy-efficient, and generations behind in speed and functionality. By featuring COTS devices to perform the critical data processing, supported by simpler RadHard devices that monitor and manage the COTS devices, and augmented with novel uses of fault-tolerant hardware, software, information, and networking within and between COTS devices, the resulting system can maximize performance and reliability while minimizing energy consumption and cost. NASA Goddard has adopted the CSP concept and technology with plans underway to feature flight-ready CSP boards on two upcoming space missions.

Target recognition based on convolutional neural network

NASA Astrophysics Data System (ADS)

Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

2017-11-01

One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

75 FR 57521 - Networking and Information Technology Research and Development (NITRD) Program: Draft NITRD 2010...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-21

... NATIONAL SCIENCE FOUNDATION Networking and Information Technology Research and Development (NITRD...) for Networking and Information Technology Research and Development (NITRD). ACTION: Notice, request.... SUMMARY: With this notice, the National Coordination Office for Networking and Information Technology...

Neural network tracking and extension of positive tracking periods

NASA Technical Reports Server (NTRS)

Hanan, Jay C.; Chao, Tien-Hsin; Moreels, Pierre

2004-01-01

Feature detectors have been considered for the role of supplying additional information to a neural network tracker. The feature detector focuses on areas of the image with significant information. Basically, if a picture says a thousand words, the feature detectors are looking for the key phrases (keypoints). These keypoints are rotationally invariant and may be matched across frames. Application of these advanced feature detectors to the neural network tracking system at JPL has promising potential. As part of an ongoing program, an advanced feature detector was tested for augmentation of a neural network based tracker. The advance feature detector extended tracking periods in test sequences including aircraft tracking, rover tracking, and simulated Martian landing. Future directions of research are also discussed.

Neural network tracking and extension of positive tracking periods

NASA Astrophysics Data System (ADS)

Hanan, Jay C.; Chao, Tien-Hsin; Moreels, Pierre

2004-04-01

Feature detectors have been considered for the role of supplying additional information to a neural network tracker. The feature detector focuses on areas of the image with significant information. Basically, if a picture says a thousand words, the feature detectors are looking for the key phrases (keypoints). These keypoints are rotationally invariant and may be matched across frames. Application of these advanced feature detectors to the neural network tracking system at JPL has promising potential. As part of an ongoing program, an advanced feature detector was tested for augmentation of a neural network based tracker. The advance feature detector extended tracking periods in test sequences including aircraft tracking, rover tracking, and simulated Martian landing. Future directions of research are also discussed.

[Anesthesia and Consciousness].

PubMed

Ogino, Yuichi; Kawamichi, Hiroaki; Saiot, Shigeru

2016-05-01

The mechanism of consciousness and loss of conciousness by general anesthetics are crucial issue for the anesthesiologists. Recent non-invasive brain-imaging technology brings about light to various our emotions and sensations in human brain; however, neural correlate of consciousness is not yet still elucidated. The concept "the seat of the consciousness (is in the subcortical nuclei)" is now completely denied, but instead the consciousness is based on the idea that connectivity and communications across cortical and thalamocortical networks. Anesthetics and sleep disrupt the networks that encompass complexity and integration. The compatibility between complexity and integration is the key feature of the consciousness, which is represented by complex, extensive, communicative and integrative electroencephalograph currents evoked by transcranial magnetic stimulation, provoking a single unified conscious experience in us, humans.

Systems Engineering Design Via Experimental Operation Research: Complex Organizational Metric for Programmatic Risk Environments (COMPRE)

NASA Technical Reports Server (NTRS)

Mog, Robert A.

1999-01-01

Unique and innovative graph theory, neural network, organizational modeling, and genetic algorithms are applied to the design and evolution of programmatic and organizational architectures. Graph theory representations of programs and organizations increase modeling capabilities and flexibility, while illuminating preferable programmatic/organizational design features. Treating programs and organizations as neural networks results in better system synthesis, and more robust data modeling. Organizational modeling using covariance structures enhances the determination of organizational risk factors. Genetic algorithms improve programmatic evolution characteristics, while shedding light on rulebase requirements for achieving specified technological readiness levels, given budget and schedule resources. This program of research improves the robustness and verifiability of systems synthesis tools, including the Complex Organizational Metric for Programmatic Risk Environments (COMPRE).

A neural network technique for remeshing of bone microstructure.

PubMed

Fischer, Anath; Holdstein, Yaron

2012-01-01

Today, there is major interest within the biomedical community in developing accurate noninvasive means for the evaluation of bone microstructure and bone quality. Recent improvements in 3D imaging technology, among them development of micro-CT and micro-MRI scanners, allow in-vivo 3D high-resolution scanning and reconstruction of large specimens or even whole bone models. Thus, the tendency today is to evaluate bone features using 3D assessment techniques rather than traditional 2D methods. For this purpose, high-quality meshing methods are required. However, the 3D meshes produced from current commercial systems usually are of low quality with respect to analysis and rapid prototyping. 3D model reconstruction of bone is difficult due to the complexity of bone microstructure. The small bone features lead to a great deal of neighborhood ambiguity near each vertex. The relatively new neural network method for mesh reconstruction has the potential to create or remesh 3D models accurately and quickly. A neural network (NN), which resembles an artificial intelligence (AI) algorithm, is a set of interconnected neurons, where each neuron is capable of making an autonomous arithmetic calculation. Moreover, each neuron is affected by its surrounding neurons through the structure of the network. This paper proposes an extension of the growing neural gas (GNN) neural network technique for remeshing a triangular manifold mesh that represents bone microstructure. This method has the advantage of reconstructing the surface of a genus-n freeform object without a priori knowledge regarding the original object, its topology, or its shape.

Geometrical features assessment of liver's tumor with application of artificial neural network evolved by imperialist competitive algorithm.

PubMed

Keshavarz, M; Mojra, A

2015-05-01

Geometrical features of a cancerous tumor embedded in biological soft tissue, including tumor size and depth, are a necessity in the follow-up procedure and making suitable therapeutic decisions. In this paper, a new socio-politically motivated global search strategy which is called imperialist competitive algorithm (ICA) is implemented to train a feed forward neural network (FFNN) to estimate the tumor's geometrical characteristics (FFNNICA). First, a viscoelastic model of liver tissue is constructed by using a series of in vitro uniaxial and relaxation test data. Then, 163 samples of the tissue including a tumor with different depths and diameters are generated by making use of PYTHON programming to link the ABAQUS and MATLAB together. Next, the samples are divided into 123 samples as training dataset and 40 samples as testing dataset. Training inputs of the network are mechanical parameters extracted from palpation of the tissue through a developing noninvasive technology called artificial tactile sensing (ATS). Last, to evaluate the FFNNICA performance, outputs of the network including tumor's depth and diameter are compared with desired values for both training and testing datasets. Deviations of the outputs from desired values are calculated by a regression analysis. Statistical analysis is also performed by measuring Root Mean Square Error (RMSE) and Efficiency (E). RMSE in diameter and depth estimations are 0.50 mm and 1.49, respectively, for the testing dataset. Results affirm that the proposed optimization algorithm for training neural network can be useful to characterize soft tissue tumors accurately by employing an artificial palpation approach. Copyright © 2015 John Wiley & Sons, Ltd.

SBIR Technology Applications to Space Communications and Navigation (SCaN)

NASA Technical Reports Server (NTRS)

Liebrecht, Phil; Eblen, Pat; Rush, John; Tzinis, Irene

2010-01-01

This slide presentation reviews the mission of the Space Communications and Navigation (SCaN) Office with particular emphasis on opportunities for technology development with SBIR companies. The SCaN office manages NASA's space communications and navigation networks: the Near Earth Network (NEN), the Space Network (SN), and the Deep Space Network (DSN). The SCaN networks nodes are shown on a world wide map and the networks are described. Two types of technologies are described: Pull technology, and Push technologies. A listing of technology themes is presented, with a discussion on Software defined Radios, Optical Communications Technology, and Lunar Lasercom Space Terminal (LLST). Other technologies that are being investigated are some Game Changing Technologies (GCT) i.e., technologies that offer the potential for improving comm. or nav. performance to the point that radical new mission objectives are possible, such as Superconducting Quantum Interference Filters, Silicon Nanowire Optical Detectors, and Auto-Configuring Cognitive Communications

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

PubMed Central

Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

PubMed

Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

A scaleable integrated sensing and control system for NDE, monitoring, and control of medium to very large composite smart structures

NASA Astrophysics Data System (ADS)

Jones, Jerry; Rhoades, Valerie; Arner, Radford; Clem, Timothy; Cuneo, Adam

2007-04-01

NDE measurements, monitoring, and control of smart and adaptive composite structures requires that the central knowledge system have an awareness of the entire structure. Achieving this goal necessitates the implementation of an integrated network of significant numbers of sensors. Additionally, in order to temporally coordinate the data from specially distributed sensors, the data must be time relevant. Early adoption precludes development of sensor technology specifically for this application, instead it will depend on the ability to utilize legacy systems. Partially supported by the U.S. Department of Commerce, National Institute of Standards and Technology, Advanced Technology Development Program (NIST-ATP), a scalable integrated system has been developed to implement monitoring of structural integrity and the control of adaptive/intelligent structures. The project, called SHIELD (Structural Health Identification and Electronic Life Determination), was jointly undertaken by: Caterpillar, N.A. Tech., Motorola, and Microstrain. SHIELD is capable of operation with composite structures, metallic structures, or hybrid structures. SHIELD consists of a real-time processing core on a Motorola MPC5200 using a C language based real-time operating system (RTOS). The RTOS kernel was customized to include a virtual backplane which makes the system completely scalable. This architecture provides for multiple processes to be operating simultaneously. They may be embedded as multiple threads on the core hardware or as separate independent processors connected to the core using a software driver called a NAT-Network Integrator (NATNI). NATNI's can be created for any communications application. In it's current embodiment, NATNI's have been created for CAN bus, TCP/IP (Ethernet) - both wired and 802.11 b and g, and serial communications using RS485 and RS232. Since SHIELD uses standard C language, it is easy to port any monitoring or control algorithm, thus providing for legacy technology which may use other hardware processors and various communications means. For example, two demonstrations of SHIELD have been completed, in January and May 2005 respectively. One demonstration used algorithms in C running in multiple threads in the SHIELD core and utilizing two different sensor networks, one CAN bus and one wireless. The second had algorithms operating in C on the SHIELD core and other algorithms running on multiple Texas Instruments DSP processors using a NATNI that communicated via wired TCP/IP. A key feature of SHIELD is the implementation of a wireless ZIGBEE (802.15.4) network for implementing large numbers of small, low cost, low power sensors communication via a meshstar wireless network. While SHIELD was designed to integrate with a wide variety of existing communications protocols, a ZIGBEE network capability was implemented specifically for SHIELD. This will facilitate the monitoring of medium to very large structures including marine applications, utility scale multi-megawatt wind energy systems, and aircraft/spacecraft. The SHIELD wireless network will facilitate large numbers of sensors (up to 32000), accommodate sensors embedded into the composite material, can communicate to both sensors and actuators, and prevents obsolescence by providing for re-programming of the nodes via remote RF communications. The wireless network provides for ultra-low energy use, spatial location, and accurate timestamping, utilizing the beaconing feature of ZIGBEE.

Simulating Operation of a Complex Sensor Network

NASA Technical Reports Server (NTRS)

Jennings, Esther; Clare, Loren; Woo, Simon

2008-01-01

Simulation Tool for ASCTA Microsensor Network Architecture (STAMiNA) ["ASCTA" denotes the Advanced Sensors Collaborative Technology Alliance.] is a computer program for evaluating conceptual sensor networks deployed over terrain to provide military situational awareness. This or a similar program is needed because of the complexity of interactions among such diverse phenomena as sensing and communication portions of a network, deployment of sensor nodes, effects of terrain, data-fusion algorithms, and threat characteristics. STAMiNA is built upon a commercial network-simulator engine, with extensions to include both sensing and communication models in a discrete-event simulation environment. Users can define (1) a mission environment, including terrain features; (2) objects to be sensed; (3) placements and modalities of sensors, abilities of sensors to sense objects of various types, and sensor false alarm rates; (4) trajectories of threatening objects; (5) means of dissemination and fusion of data; and (6) various network configurations. By use of STAMiNA, one can simulate detection of targets through sensing, dissemination of information by various wireless communication subsystems under various scenarios, and fusion of information, incorporating such metrics as target-detection probabilities, false-alarm rates, and communication loads, and capturing effects of terrain and threat.

An Internet of Things Approach to Electrical Power Monitoring and Outage Reporting

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

Koch, Daniel B

The so-called Internet of Things concept has captured much attention recently as ordinary devices are connected to the Internet for monitoring and control purposes. One enabling technology is the proliferation of low-cost, single board computers with built-in network interfaces. Some of these are capable of hosting full-fledged operating systems that provide rich programming environments. Taken together, these features enable inexpensive solutions for even traditional tasks such as the one presented here for electrical power monitoring and outage reporting.

Automated video surveillance: teaching an old dog new tricks

NASA Astrophysics Data System (ADS)

McLeod, Alastair

1993-12-01

The automated video surveillance market is booming with new players, new systems, new hardware and software, and an extended range of applications. This paper reviews available technology, and describes the features required for a good automated surveillance system. Both hardware and software are discussed. An overview of typical applications is also given. A shift towards PC-based hybrid systems, use of parallel processing, neural networks, and exploitation of modern telecomms are introduced, highlighting the evolution modern video surveillance systems.

Multimodality imaging and state-of-art GPU technology in discriminating benign from malignant breast lesions on real time decision support system

NASA Astrophysics Data System (ADS)

Kostopoulos, S.; Sidiropoulos, K.; Glotsos, D.; Dimitropoulos, N.; Kalatzis, I.; Asvestas, P.; Cavouras, D.

2014-03-01

The aim of this study was to design a pattern recognition system for assisting the diagnosis of breast lesions, using image information from Ultrasound (US) and Digital Mammography (DM) imaging modalities. State-of-art computer technology was employed based on commercial Graphics Processing Unit (GPU) cards and parallel programming. An experienced radiologist outlined breast lesions on both US and DM images from 59 patients employing a custom designed computer software application. Textural features were extracted from each lesion and were used to design the pattern recognition system. Several classifiers were tested for highest performance in discriminating benign from malignant lesions. Classifiers were also combined into ensemble schemes for further improvement of the system's classification accuracy. Following the pattern recognition system optimization, the final system was designed employing the Probabilistic Neural Network classifier (PNN) on the GPU card (GeForce 580GTX) using CUDA programming framework and C++ programming language. The use of such state-of-art technology renders the system capable of redesigning itself on site once additional verified US and DM data are collected. Mixture of US and DM features optimized performance with over 90% accuracy in correctly classifying the lesions.

Virtualizing intimacy: information communication technologies and transnational families in therapy.

PubMed

Bacigalupe, Gonzalo; Lambe, Susan

2011-03-01

Information communication technologies (ICTs) are a ubiquitous feature of immigrant family life. Affordable, widely accessible, and highly adaptable ICTs have transformed the immigrant experience into a transnational process with family networks redesigned but not lost. Being a transnational family is not a new phenomenon. Transnationalism, however, has historically been reserved for the wealthier professional and political immigrant class who were able to freely travel and use expensive forms of communication before the emergence of accessible technologies. This paper systematically reviews the research literature to investigate the potential impact of ICTs on the lives of transnational families and how these families utilize them. The wide penetration of ICTs also puts into question some of the ways in which scholars have conceptualized the immigrant experience. The appropriate use of technology in family therapy should strengthen culturally competent and equity-based approaches to address the needs of these families. A family therapy with a transnational family illuminates some of the potential that these technologies introduce in the practice of relational clinicians. 2011 © FPI, Inc.

Game On, Science - How Video Game Technology May Help Biologists Tackle Visualization Challenges

PubMed Central

Da Silva, Franck; Empereur-mot, Charly; Chavent, Matthieu; Baaden, Marc

2013-01-01

The video games industry develops ever more advanced technologies to improve rendering, image quality, ergonomics and user experience of their creations providing very simple to use tools to design new games. In the molecular sciences, only a small number of experts with specialized know-how are able to design interactive visualization applications, typically static computer programs that cannot easily be modified. Are there lessons to be learned from video games? Could their technology help us explore new molecular graphics ideas and render graphics developments accessible to non-specialists? This approach points to an extension of open computer programs, not only providing access to the source code, but also delivering an easily modifiable and extensible scientific research tool. In this work, we will explore these questions using the Unity3D game engine to develop and prototype a biological network and molecular visualization application for subsequent use in research or education. We have compared several routines to represent spheres and links between them, using either built-in Unity3D features or our own implementation. These developments resulted in a stand-alone viewer capable of displaying molecular structures, surfaces, animated electrostatic field lines and biological networks with powerful, artistic and illustrative rendering methods. We consider this work as a proof of principle demonstrating that the functionalities of classical viewers and more advanced novel features could be implemented in substantially less time and with less development effort. Our prototype is easily modifiable and extensible and may serve others as starting point and platform for their developments. A webserver example, standalone versions for MacOS X, Linux and Windows, source code, screen shots, videos and documentation are available at the address: http://unitymol.sourceforge.net/. PMID:23483961

Game on, science - how video game technology may help biologists tackle visualization challenges.

PubMed

Lv, Zhihan; Tek, Alex; Da Silva, Franck; Empereur-mot, Charly; Chavent, Matthieu; Baaden, Marc

2013-01-01

The video games industry develops ever more advanced technologies to improve rendering, image quality, ergonomics and user experience of their creations providing very simple to use tools to design new games. In the molecular sciences, only a small number of experts with specialized know-how are able to design interactive visualization applications, typically static computer programs that cannot easily be modified. Are there lessons to be learned from video games? Could their technology help us explore new molecular graphics ideas and render graphics developments accessible to non-specialists? This approach points to an extension of open computer programs, not only providing access to the source code, but also delivering an easily modifiable and extensible scientific research tool. In this work, we will explore these questions using the Unity3D game engine to develop and prototype a biological network and molecular visualization application for subsequent use in research or education. We have compared several routines to represent spheres and links between them, using either built-in Unity3D features or our own implementation. These developments resulted in a stand-alone viewer capable of displaying molecular structures, surfaces, animated electrostatic field lines and biological networks with powerful, artistic and illustrative rendering methods. We consider this work as a proof of principle demonstrating that the functionalities of classical viewers and more advanced novel features could be implemented in substantially less time and with less development effort. Our prototype is easily modifiable and extensible and may serve others as starting point and platform for their developments. A webserver example, standalone versions for MacOS X, Linux and Windows, source code, screen shots, videos and documentation are available at the address: http://unitymol.sourceforge.net/.

Innovative Seismoeletromagnetic Research at the front of the Hellenic Arc

NASA Astrophysics Data System (ADS)

Makris, John P.; Chiappini, Massimo; Nardi, Adriano; Carluccio, Roberto; Rigakis, Hercules; Hloupis, George; Fragkiadakis, Kostantinos; Pentaris, Fragkiskos; Saltas, Vassilios; Vallianatos, Filippos

2013-04-01

Taking into account the complex nature and rarity of strong seismic events, as well as the form multiplicity and timing variety of possible preseismic signatures, the predominant view of the scientific community still seems nowadays to lean against earthquake prediction, especially the short-term one. On the other hand, seismoelectromagnetic (SEM) research appears to be a promising approach to earthquake prediction research. In this context, the project TeCH-SEM [Technologies Coalescence for Holistic Seismoelectromagnetic Research (Lithosphere-Atmosphere-Ionosphere Coupling)] aims to establish an integrated approach to SEM investigation, by developing and implementing novel-innovative technologies for the study of pre-seismic electric, magnetic and electromagnetic signatures in a broadband spectrum (ULF-ELF-VLF-LF-HF). In this framework, at the natural laboratory of the seismically active south- and south-western part of the Hellenic Arc (broader region of Crete) is being developed a permanent network of ULF-ELF seismoelectromagnetic stations featuring novel design that provides real-time telemetry, extended autonomy, light-weight and small-size but robust and powerful datalogging and self-diagnostics for reliable, long-term operation. This network is complemented by the simultaneous deployment of an innovative ELF-VLF seismoelectromagnetic telemetric network that will attempt to detect, in real conditions, VLF electromagnetic transients that have been repeatedly observed in the laboratory to be emitted from rock samples with various lithologies subjected to fracture under uniaxial compression. Both networks, it is anticipated to remain in operation for many years. Acknowledgements This research is implemented in the framework of the project entitled "Technologies Coalescence for Holistic Seismoelectromagnetic Research (Lithosphere-Atmosphere-Ionosphere Coupling)" of the Archimedes III Call through the Operational Program "Education and Lifelong Learning" and is co-financed by the European Union (European Social Fund) and Greek national funds.

The 2010 AOP Workshop Summary Report

NASA Technical Reports Server (NTRS)

Hooker, Stanford B.; Morrow, John H.; Brown, James W.; Firestone, Elaine R.

2011-01-01

The rationale behind the current workshop, which was hosted by Biospherical Instruments Inc. (BSI), was to update the community and get community input with respect to the following: topics not addressed during the first workshop, specifically the processing of above-water apparent optical property (AOP data) within the Processing of Radiometric Observations of Seawater using Information Technologies (PROSIT) architecture; PROSIT data processing issues that have developed or tasks that have been completed, since the first workshop; and NASA instrumentation developments, both above- and in-water, that are relevant to both workshops and next generation mission planning. The workshop emphasized presentations on new AOP instrumentation, desired and required features for processing above-water measurements of the AOPs of seawater, working group discussions, and a community update for the in-water data processing already present in PROSIT. The six working groups were organized as follows: a) data ingest and data products; b) required and desired features for optically shallow and optically deep waters; c) contamination rejection (clouds), corrections, and data filtering; d) sun photometry and polarimetry; e) instrumentation networks; and f) hyperspectral versus fixed-wavelength sensors. The instrumentation networks working group was intended to provide more detailed information about desired and required features of autonomous sampling systems. Plenary discussions produced a number of recommendations for evolving and documenting PROSIT.

Hadoop neural network for parallel and distributed feature selection.

PubMed

Hodge, Victoria J; O'Keefe, Simon; Austin, Jim

2016-06-01

In this paper, we introduce a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of five feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop YARN. Hadoop allows parallel and distributed processing. Each feature selector can be divided into subtasks and the subtasks can then be processed in parallel. Multiple feature selectors can also be processed simultaneously (in parallel) allowing multiple feature selectors to be compared. We identify commonalities among the five features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all five feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Preparing for an aging population and improving chronic disease management.

PubMed

Dexter, Paul R; Miller, Douglas K; Clark, Daniel O; Weiner, Michael; Harris, Lisa E; Livin, Lee; Myers, Isaac; Shaw, David; Blue, Lee Ann; Kunzer, John; Overhage, J Marc

2010-11-13

New models of health care delivery are inevitable. There is likely to be increasing emphasis on patient self-monitoring, health care delivery at patient homes, interdisciplinary treatment plans, a greater percentage of medical care delivered by non-physician health professionals, targeted health educational materials, and greater involvement and training of informal caregivers. The Information Technologies (IT) infrastructure of health systems will need to adapt. We have begun sorting out the implications of this future within a County public hospital system: defining the desirable features, relevant technologies, necessary modifications to the network, and additional data elements to be captured. We seek to build an infrastructure that will support new patient-focused technologies designed to more efficiently and effectively support older individuals. We hypothesize utility to further exploring the impact that new health care delivery models will have on health systems' IT infrastructures.

Hypotheses generation as supervised link discovery with automated class labeling on large-scale biomedical concept networks

PubMed Central

2012-01-01

Computational approaches to generate hypotheses from biomedical literature have been studied intensively in recent years. Nevertheless, it still remains a challenge to automatically discover novel, cross-silo biomedical hypotheses from large-scale literature repositories. In order to address this challenge, we first model a biomedical literature repository as a comprehensive network of biomedical concepts and formulate hypotheses generation as a process of link discovery on the concept network. We extract the relevant information from the biomedical literature corpus and generate a concept network and concept-author map on a cluster using Map-Reduce frame-work. We extract a set of heterogeneous features such as random walk based features, neighborhood features and common author features. The potential number of links to consider for the possibility of link discovery is large in our concept network and to address the scalability problem, the features from a concept network are extracted using a cluster with Map-Reduce framework. We further model link discovery as a classification problem carried out on a training data set automatically extracted from two network snapshots taken in two consecutive time duration. A set of heterogeneous features, which cover both topological and semantic features derived from the concept network, have been studied with respect to their impacts on the accuracy of the proposed supervised link discovery process. A case study of hypotheses generation based on the proposed method has been presented in the paper. PMID:22759614

Collaborative classification of hyperspectral and visible images with convolutional neural network

NASA Astrophysics Data System (ADS)

Zhang, Mengmeng; Li, Wei; Du, Qian

2017-10-01

Recent advances in remote sensing technology have made multisensor data available for the same area, and it is well-known that remote sensing data processing and analysis often benefit from multisource data fusion. Specifically, low spatial resolution of hyperspectral imagery (HSI) degrades the quality of the subsequent classification task while using visible (VIS) images with high spatial resolution enables high-fidelity spatial analysis. A collaborative classification framework is proposed to fuse HSI and VIS images for finer classification. First, the convolutional neural network model is employed to extract deep spectral features for HSI classification. Second, effective binarized statistical image features are learned as contextual basis vectors for the high-resolution VIS image, followed by a classifier. The proposed approach employs diversified data in a decision fusion, leading to an integration of the rich spectral information, spatial information, and statistical representation information. In particular, the proposed approach eliminates the potential problems of the curse of dimensionality and excessive computation time. The experiments evaluated on two standard data sets demonstrate better classification performance offered by this framework.

A 181 GOPS AKAZE Accelerator Employing Discrete-Time Cellular Neural Networks for Real-Time Feature Extraction.

PubMed

Jiang, Guangli; Liu, Leibo; Zhu, Wenping; Yin, Shouyi; Wei, Shaojun

2015-09-04

This paper proposes a real-time feature extraction VLSI architecture for high-resolution images based on the accelerated KAZE algorithm. Firstly, a new system architecture is proposed. It increases the system throughput, provides flexibility in image resolution, and offers trade-offs between speed and scaling robustness. The architecture consists of a two-dimensional pipeline array that fully utilizes computational similarities in octaves. Secondly, a substructure (block-serial discrete-time cellular neural network) that can realize a nonlinear filter is proposed. This structure decreases the memory demand through the removal of data dependency. Thirdly, a hardware-friendly descriptor is introduced in order to overcome the hardware design bottleneck through the polar sample pattern; a simplified method to realize rotation invariance is also presented. Finally, the proposed architecture is designed in TSMC 65 nm CMOS technology. The experimental results show a performance of 127 fps in full HD resolution at 200 MHz frequency. The peak performance reaches 181 GOPS and the throughput is double the speed of other state-of-the-art architectures.

The research and application of multi-biometric acquisition embedded system

NASA Astrophysics Data System (ADS)

Deng, Shichao; Liu, Tiegen; Guo, Jingjing; Li, Xiuyan

2009-11-01

The identification technology based on multi-biometric can greatly improve the applicability, reliability and antifalsification. This paper presents a multi-biometric system bases on embedded system, which includes: three capture daughter boards are applied to obtain different biometric: one each for fingerprint, iris and vein of the back of hand; FPGA (Field Programmable Gate Array) is designed as coprocessor, which uses to configure three daughter boards on request and provides data path between DSP (digital signal processor) and daughter boards; DSP is the master processor and its functions include: control the biometric information acquisition, extracts feature as required and responsible for compare the results with the local database or data server through network communication. The advantages of this system were it can acquire three different biometric in real time, extracts complexity feature flexibly in different biometrics' raw data according to different purposes and arithmetic and network interface on the core-board will be the solution of big data scale. Because this embedded system has high stability, reliability, flexibility and fit for different data scale, it can satisfy the demand of multi-biometric recognition.

Research of the self-healing technologies in the optical communication network of distribution automation

NASA Astrophysics Data System (ADS)

Wang, Hao; Zhong, Guoxin

2018-03-01

Optical communication network is the mainstream technique of the communication networks for distribution automation, and self-healing technologies can improve the in reliability of the optical communication networks significantly. This paper discussed the technical characteristics and application scenarios of several network self-healing technologies in the access layer, the backbone layer and the core layer of the optical communication networks for distribution automation. On the base of the contrastive analysis, this paper gives an application suggestion of these self-healing technologies.

Technology Network Ties: Network Services and Technology Programs for New York State's Educational System.

ERIC Educational Resources Information Center

New York State Education Dept., Albany. Office of Elementary and Secondary Education Planning, Testing, and Technological Services.

The New York State Technology Network Ties (TNT) systems is a statewide telecommunications network which consists of computers, telephone lines, and telecommunications hardware and software. This network links school districts, Boards of Cooperative Educational Services (BOCES), libraries, other educational institutions, and the State Education…

Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Liu, Hong; Zheng, Bin

2016-03-01

Although mammography is the only clinically acceptable imaging modality used in the population-based breast cancer screening, its efficacy is quite controversy. One of the major challenges is how to help radiologists more accurately classify between benign and malignant lesions. The purpose of this study is to investigate a new mammographic mass classification scheme based on a deep learning method. In this study, we used an image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms, which includes 280 malignant and 280 benign mass ROIs, respectively. An eight layer deep learning network was applied, which employs three pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perception (MLP) classifier for feature categorization. In order to improve robustness of selected features, each convolution layer is connected with a max-pooling layer. A number of 20, 10, and 5 feature maps were utilized for the 1st, 2nd and 3rd convolution layer, respectively. The convolution networks are followed by a MLP classifier, which generates a classification score to predict likelihood of a ROI depicting a malignant mass. Among 560 ROIs, 420 ROIs were used as a training dataset and the remaining 140 ROIs were used as a validation dataset. The result shows that the new deep learning based classifier yielded an area under the receiver operation characteristic curve (AUC) of 0.810+/-0.036. This study demonstrated the potential superiority of using a deep learning based classifier to distinguish malignant and benign breast masses without segmenting the lesions and extracting the pre-defined image features.

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

Using mobile phone technology to provide recovery support for women offenders.

PubMed

Scott, Christy K; Johnson, Kimberly; Dennis, Michael L

2013-10-01

Mobile technology holds promise as a recovery tool for people with substance use disorders. However, some populations who may benefit the most may not have access to or experience with mobile phones. Incarcerated women represent a group at high risk for recidivism and relapse to substance abuse. Cost-effective mechanisms must be in place to support their recovery upon release. This study explores using mobile technology as a recovery management tool for women offenders residing in the community following release from jail. This study surveyed 325 minority women offenders with substance use disorders to determine whether or not they use cell phones, their comfort with texting and search features, and the social networks that they access from mobile phones. We found that 83% of survey subjects had cell phones; 30% of those were smartphones. Seventy-seven percent of the women reported access to supportive friends, and 88% had close family members they contacted regularly using mobile technology. Results indicated that most of the women were comfortable using a mobile phone, although the majority of them had prepaid minutes rather than plans, and most did currently use smartphones or have the capability to download applications or access social networks via their phones. Most women reported that they would be comfortable using a mobile phone to text, e-mail, and answer surveys. The high rate of adoption of mobile technology by women offenders makes them a promising target for recovery support delivered via mobile phone.

Contextual descriptors and neural networks for scene analysis in VHR SAR images

NASA Astrophysics Data System (ADS)

Del Frate, Fabio; Picchiani, Matteo; Falasco, Alessia; Schiavon, Giovanni

2016-10-01

The development of SAR technology during the last decade has made it possible to collect a huge amount of data over many regions of the world. In particular, the availability of SAR images from different sensors, with metric or sub-metric spatial resolution, offers novel opportunities in different fields as land cover, urban monitoring, soil consumption etc. On the other hand, automatic approaches become crucial for the exploitation of such a huge amount of information. In such a scenario, especially if single polarization images are considered, the main issue is to select appropriate contextual descriptors, since the backscattering coefficient of a single pixel may not be sufficient to classify an object on the scene. In this paper a comparison among three different approaches for contextual features definition is presented so as to design optimum procedures for VHR SAR scene understanding. The first approach is based on Gray Level Co- Occurrence Matrix since it is widely accepted and several studies have used it for land cover classification with SAR data. The second approach is based on the Fourier spectra and it has been already proposed with positive results for this kind of problems, the third one is based on Auto-associative Neural Networks which have been already proven effective for features extraction from polarimetric SAR images. The three methods are evaluated in terms of the accuracy of the classified scene when the features extracted using each method are considered as input to a neural network classificator and applied on different Cosmo-SkyMed spotlight products.

A novel paradigm for telemedicine using the personal bio-monitor.

PubMed

Bhatikar, Sanjay R; Mahajan, Roop L; DeGroff, Curt

2002-01-01

The foray of solid-state technology in the medical field has yielded an arsenal of sophisticated healthcare tools. Personal, portable computing power coupled with the information superhighway open up the possibility of sophisticated healthcare management that will impact the medical field just as much. The full synergistic potential of three interwoven technologies: (1) compact electronics, (2) World Wide Web, and (3) Artificial Intelligence is yet to be realized. The system presented in this paper integrates these technologies synergistically, providing a new paradigm for healthcare. Our idea is to deploy internet-enabled, intelligent, handheld personal computers for medical diagnosis. The salient features of the 'Personal Bio-Monitor' we envisage are: (1) Utilization of the peripheral signals of the body which may be acquired non-invasively and with ease, for diagnosis of medical conditions; (2) An Artificial Neural Network (ANN) based approach for diagnosis; (3) Configuration of the diagnostic device as a handheld for personal use; (4) Internet connectivity, following the emerging bluetooth protocol, for prompt conveyance of information to a patient's health care provider via the World Wide Web. The proposal is substantiated with an intelligent handheld device developed by the investigators for pediatric cardiac auscultation. This device performed accurate diagnoses of cardiac abnormalities in pediatrics using an artificial neural network to process heart sounds acquired by a low-frequency microphone and transmitted its diagnosis to a desktop PC via infrared. The idea of the personal biomonitor presented here has the potential to streamline healthcare by optimizing two valuable resources: physicians' time and sophisticated equipment time. We show that the elements of such a system are in place, with our prototype. Our novel contribution is the synergistic integration of compact electronics' technology, artificial neural network methodology and the wireless web resulting in a revolutionary new paradigm for healthcare management.

Feature Biases in Early Word Learning: Network Distinctiveness Predicts Age of Acquisition

ERIC Educational Resources Information Center

Engelthaler, Tomas; Hills, Thomas T.

2017-01-01

Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge…

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

NASA Technical Reports Server (NTRS)

Smith, Aaron; Evans, Michael; Downey, Joseph

2017-01-01

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Sensor and information fusion for improved hostile fire situational awareness

NASA Astrophysics Data System (ADS)

Scanlon, Michael V.; Ludwig, William D.

2010-04-01

A research-oriented Army Technology Objective (ATO) named Sensor and Information Fusion for Improved Hostile Fire Situational Awareness uniquely focuses on the underpinning technologies to detect and defeat any hostile threat; before, during, and after its occurrence. This is a joint effort led by the Army Research Laboratory, with the Armaments and the Communications and Electronics Research, Development, and Engineering Centers (CERDEC and ARDEC) partners. It addresses distributed sensor fusion and collaborative situational awareness enhancements, focusing on the underpinning technologies to detect/identify potential hostile shooters prior to firing a shot and to detect/classify/locate the firing point of hostile small arms, mortars, rockets, RPGs, and missiles after the first shot. A field experiment conducted addressed not only diverse modality sensor performance and sensor fusion benefits, but gathered useful data to develop and demonstrate the ad hoc networking and dissemination of relevant data and actionable intelligence. Represented at this field experiment were various sensor platforms such as UGS, soldier-worn, manned ground vehicles, UGVs, UAVs, and helicopters. This ATO continues to evaluate applicable technologies to include retro-reflection, UV, IR, visible, glint, LADAR, radar, acoustic, seismic, E-field, narrow-band emission and image processing techniques to detect the threats with very high confidence. Networked fusion of multi-modal data will reduce false alarms and improve actionable intelligence by distributing grid coordinates, detection report features, and imagery of threats.

Center for development technology and program in technology and human affairs. [emphasizing technology-based networks

NASA Technical Reports Server (NTRS)

Wong, M. D.

1974-01-01

The role of technology in nontraditional higher education with particular emphasis on technology-based networks is analyzed nontraditional programs, institutions, and consortia are briefly reviewed. Nontraditional programs which utilize technology are studied. Technology-based networks are surveyed and analyzed with regard to kinds of students, learning locations, technology utilization, interinstitutional relationships, cost aspects, problems, and future outlook.

Face recognition via Gabor and convolutional neural network

NASA Astrophysics Data System (ADS)

Lu, Tongwei; Wu, Menglu; Lu, Tao

2018-04-01

In recent years, the powerful feature learning and classification ability of convolutional neural network have attracted widely attention. Compared with the deep learning, the traditional machine learning algorithm has a good explanatory which deep learning does not have. Thus, In this paper, we propose a method to extract the feature of the traditional algorithm as the input of convolution neural network. In order to reduce the complexity of the network, the kernel function of Gabor wavelet is used to extract the feature from different position, frequency and direction of target image. It is sensitive to edge of image which can provide good direction and scale selection. The extraction of the image from eight directions on a scale are as the input of network that we proposed. The network have the advantage of weight sharing and local connection and texture feature of the input image can reduce the influence of facial expression, gesture and illumination. At the same time, we introduced a layer which combined the results of the pooling and convolution can extract deeper features. The training network used the open source caffe framework which is beneficial to feature extraction. The experiment results of the proposed method proved that the network structure effectively overcame the barrier of illumination and had a good robustness as well as more accurate and rapid than the traditional algorithm.

Social networking in online support groups for health: how online social networking benefits patients.

PubMed

Chung, Jae Eun

2014-01-01

An increasing number of online support groups (OSGs) have embraced the features of social networking. So far, little is known about how patients use and benefit from these features. By implementing the uses-and-gratifications framework, the author conducted an online survey with current users of OSGs to examine associations among motivation, use of specific features of OSG, and support outcomes. Findings suggest that OSG users make selective use of varied features depending on their needs, and that perceptions of receiving emotional and informational support are associated more with the use of some features than others. For example, those with strong motivation for social interaction use diverse features of OSG and make one-to-one connections with other users by friending. In contrast, those with strong motivation for information seeking limit their use primarily to discussion boards. Results also show that online social networking features, such as friending and sharing of personal stories on blogs, are helpful in satisfying the need for emotional support. The present study sheds light on online social networking features in the context of health-related OSGs and provides practical lessons on how to improve the capacity of OSGs to serve the needs of their users.

Integrating epigenomic data and 3D genomic structure with a new measure of chromatin assortativity.

PubMed

Pancaldi, Vera; Carrillo-de-Santa-Pau, Enrique; Javierre, Biola Maria; Juan, David; Fraser, Peter; Spivakov, Mikhail; Valencia, Alfonso; Rico, Daniel

2016-07-08

Network analysis is a powerful way of modeling chromatin interactions. Assortativity is a network property used in social sciences to identify factors affecting how people establish social ties. We propose a new approach, using chromatin assortativity, to integrate the epigenomic landscape of a specific cell type with its chromatin interaction network and thus investigate which proteins or chromatin marks mediate genomic contacts. We use high-resolution promoter capture Hi-C and Hi-Cap data as well as ChIA-PET data from mouse embryonic stem cells to investigate promoter-centered chromatin interaction networks and calculate the presence of specific epigenomic features in the chromatin fragments constituting the nodes of the network. We estimate the association of these features with the topology of four chromatin interaction networks and identify features localized in connected areas of the network. Polycomb group proteins and associated histone marks are the features with the highest chromatin assortativity in promoter-centered networks. We then ask which features distinguish contacts amongst promoters from contacts between promoters and other genomic elements. We observe higher chromatin assortativity of the actively elongating form of RNA polymerase 2 (RNAPII) compared with inactive forms only in interactions between promoters and other elements. Contacts among promoters and between promoters and other elements have different characteristic epigenomic features. We identify a possible role for the elongating form of RNAPII in mediating interactions among promoters, enhancers, and transcribed gene bodies. Our approach facilitates the study of multiple genome-wide epigenomic profiles, considering network topology and allowing the comparison of chromatin interaction networks.

Modeling of the ground-to-SSFMB link networking features using SPW

NASA Technical Reports Server (NTRS)

Watson, John C.

1993-01-01

This report describes the modeling and simulation of the networking features of the ground-to-Space Station Freedom manned base (SSFMB) link using COMDISCO signal processing work-system (SPW). The networking features modeled include the implementation of Consultative Committee for Space Data Systems (CCSDS) protocols in the multiplexing of digitized audio and core data into virtual channel data units (VCDU's) in the control center complex and the demultiplexing of VCDU's in the onboard baseband signal processor. The emphasis of this work has been placed on techniques for modeling the CCSDS networking features using SPW. The objectives for developing the SPW models are to test the suitability of SPW for modeling networking features and to develop SPW simulation models of the control center complex and space station baseband signal processor for use in end-to-end testing of the ground-to-SSFMB S-band single access forward (SSAF) link.

Biomorphic networks: approach to invariant feature extraction and segmentation for ATR

NASA Astrophysics Data System (ADS)

Baek, Andrew; Farhat, Nabil H.

1998-10-01

Invariant features in two dimensional binary images are extracted in a single layer network of locally coupled spiking (pulsating) model neurons with prescribed synapto-dendritic response. The feature vector for an image is represented as invariant structure in the aggregate histogram of interspike intervals obtained by computing time intervals between successive spikes produced from each neuron over a given period of time and combining such intervals from all neurons in the network into a histogram. Simulation results show that the feature vectors are more pattern-specific and invariant under translation, rotation, and change in scale or intensity than achieved in earlier work. We also describe an application of such networks to segmentation of line (edge-enhanced or silhouette) images. The biomorphic spiking network's capabilities in segmentation and invariant feature extraction may prove to be, when they are combined, valuable in Automated Target Recognition (ATR) and other automated object recognition systems.

Using input feature information to improve ultraviolet retrieval in neural networks

NASA Astrophysics Data System (ADS)

Sun, Zhibin; Chang, Ni-Bin; Gao, Wei; Chen, Maosi; Zempila, Melina

2017-09-01

In neural networks, the training/predicting accuracy and algorithm efficiency can be improved significantly via accurate input feature extraction. In this study, some spatial features of several important factors in retrieving surface ultraviolet (UV) are extracted. An extreme learning machine (ELM) is used to retrieve the surface UV of 2014 in the continental United States, using the extracted features. The results conclude that more input weights can improve the learning capacities of neural networks.

Schizophrenia classification using functional network features

NASA Astrophysics Data System (ADS)

Rish, Irina; Cecchi, Guillermo A.; Heuton, Kyle

2012-03-01

This paper focuses on discovering statistical biomarkers (features) that are predictive of schizophrenia, with a particular focus on topological properties of fMRI functional networks. We consider several network properties, such as node (voxel) strength, clustering coefficients, local efficiency, as well as just a subset of pairwise correlations. While all types of features demonstrate highly significant statistical differences in several brain areas, and close to 80% classification accuracy, the most remarkable results of 93% accuracy are achieved by using a small subset of only a dozen of most-informative (lowest p-value) correlation features. Our results suggest that voxel-level correlations and functional network features derived from them are highly informative about schizophrenia and can be used as statistical biomarkers for the disease.

Micro-electro-mechanical systems (MEMS) and agile lensing-based modules for communications, sensing and signal processing

NASA Astrophysics Data System (ADS)

Reza, Syed Azer

This dissertation proposes the use of the emerging Micro-Electro-Mechanical Systems (MEMS) and agile lensing optical device technologies to design novel and powerful signal conditioning and sensing modules for advanced applications in optical communications, physical parameter sensing and RF/optical signal processing. For example, these new module designs have experimentally demonstrated exceptional features such as stable loss broadband operations and high > 60 dB optical dynamic range signal filtering capabilities. The first part of the dissertation describes the design and demonstration of digital MEMS-based signal processing modules for communication systems and sensor networks using the TI DLP (Digital Light Processing) technology. Examples of such modules include optical power splitters, narrowband and broadband variable fiber optical attenuators, spectral shapers and filters. Compared to prior works, these all-digital designs have advantages of repeatability, accuracy, and reliability that are essential for advanced communications and sensor applications. The next part of the dissertation proposes, analyzes and demonstrates the use of analog opto-fluidic agile lensing technology for sensor networks and test and measurement systems. Novel optical module designs for distance sensing, liquid level sensing, three-dimensional object shape sensing and variable photonic delay lines are presented and experimentally demonstrated. Compared to prior art module designs, the proposed analog-mode modules have exceptional performances, particularly for extreme environments (e.g., caustic liquids) where the free-space agile beam-based sensor provide remote non-contact access for physical sensing operations. The dissertation also presents novel modules involving hybrid analog-digital photonic designs that make use of the different optical device technologies to deliver the best features of both analog and digital optical device operations and controls. Digital controls are achieved through the use of the digital MEMS technology and analog controls are realized by employing opto-fluidic agile lensing technology and acousto-optic technology. For example, variable fiber-optic attenuators and spectral filters are proposed using the hybrid design. Compared to prior art module designs, these hybrid designs provide a higher module dynamic range and increased resolution that are critical in various advanced system applications. In summary, the dissertation shows the added power of hybrid optical designs using both the digital and analog photonic signal processing versus just all-digital or all-analog module designs.

Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data.

PubMed

Chatterjee, Sankhadeep; Dey, Nilanjan; Shi, Fuqian; Ashour, Amira S; Fong, Simon James; Sen, Soumya

2018-04-01

Dengue fever detection and classification have a vital role due to the recent outbreaks of different kinds of dengue fever. Recently, the advancement in the microarray technology can be employed for such classification process. Several studies have established that the gene selection phase takes a significant role in the classifier performance. Subsequently, the current study focused on detecting two different variations, namely, dengue fever (DF) and dengue hemorrhagic fever (DHF). A modified bag-of-features method has been proposed to select the most promising genes in the classification process. Afterward, a modified cuckoo search optimization algorithm has been engaged to support the artificial neural (ANN-MCS) to classify the unknown subjects into three different classes namely, DF, DHF, and another class containing convalescent and normal cases. The proposed method has been compared with other three well-known classifiers, namely, multilayer perceptron feed-forward network (MLP-FFN), artificial neural network (ANN) trained with cuckoo search (ANN-CS), and ANN trained with PSO (ANN-PSO). Experiments have been carried out with different number of clusters for the initial bag-of-features-based feature selection phase. After obtaining the reduced dataset, the hybrid ANN-MCS model has been employed for the classification process. The results have been compared in terms of the confusion matrix-based performance measuring metrics. The experimental results indicated a highly statistically significant improvement with the proposed classifier over the traditional ANN-CS model.

Cross-modal integration of lexical-semantic features during word processing: evidence from oscillatory dynamics during EEG.

PubMed

van Ackeren, Markus J; Rueschemeyer, Shirley-Ann

2014-01-01

In recent years, numerous studies have provided converging evidence that word meaning is partially stored in modality-specific cortical networks. However, little is known about the mechanisms supporting the integration of this distributed semantic content into coherent conceptual representations. In the current study we aimed to address this issue by using EEG to look at the spatial and temporal dynamics of feature integration during word comprehension. Specifically, participants were presented with two modality-specific features (i.e., visual or auditory features such as silver and loud) and asked to verify whether these two features were compatible with a subsequently presented target word (e.g., WHISTLE). Each pair of features described properties from either the same modality (e.g., silver, tiny  =  visual features) or different modalities (e.g., silver, loud  =  visual, auditory). Behavioral and EEG data were collected. The results show that verifying features that are putatively represented in the same modality-specific network is faster than verifying features across modalities. At the neural level, integrating features across modalities induces sustained oscillatory activity around the theta range (4-6 Hz) in left anterior temporal lobe (ATL), a putative hub for integrating distributed semantic content. In addition, enhanced long-range network interactions in the theta range were seen between left ATL and a widespread cortical network. These results suggest that oscillatory dynamics in the theta range could be involved in integrating multimodal semantic content by creating transient functional networks linking distributed modality-specific networks and multimodal semantic hubs such as left ATL.

An Overview of SBIR Phase 2 Communications Technology and Development

NASA Technical Reports Server (NTRS)

Nguyen, Hung D.; Steele, Gynelle C.

2015-01-01

Technological innovation is the overall focus of NASA's Small Business Innovation Research (SBIR) program. The program invests in the development of innovative concepts and technologies to help NASA's mission directorates address critical research and development needs for agency projects. This report highlights innovative SBIR Phase II projects from 2007-2012 specifically addressing areas in Communications Technology and Development which is one of six core competencies at NASA Glenn Research Center. There are eighteen technologies featured with emphasis on a wide spectrum of applications such as with a security-enhanced autonomous network management, secure communications using on-demand single photons, cognitive software-defined radio, spacesuit audio systems, multiband photonic phased-array antenna, and much more. Each article in this booklet describes an innovation, technical objective, and highlights NASA commercial and industrial applications. This report serves as an opportunity for NASA personnel including engineers, researchers, and program managers to learn of NASA SBIR's capabilities that might be crosscutting into this technology area. As the result, it would cause collaborations and partnerships between the small companies and NASA Programs and Projects resulting in benefit to both SBIR companies and NASA.

Advancing the State of the Art in Applying Network Science to C2

DTIC Science & Technology

2014-06-01

technological networks to include information , cognitive and social networks, they have yet to apply the full range of theoretical instruments now...robustness, and processes. While NEC researchers extended their coverage from technological networks to include information , cognitive and social networks...can be found in a wide variety of domains. For example, Newman (2003) surveys work on biological, technological , information , and social networks

Proceedings of a Conference on Telecommunication Technologies, Networkings and Libraries

NASA Astrophysics Data System (ADS)

Knight, N. K.

1981-12-01

Current and developing technologies for digital transmission of image data likely to have an impact on the operations of libraries and information centers or provide support for information networking are reviewed. Technologies reviewed include slow scan television, teleconferencing, and videodisc technology and standards development for computer network interconnection through hardware and software, particularly packet switched networks computer network protocols for library and information service applications, the structure of a national bibliographic telecommunications network; and the major policy issues involved in the regulation or deregulation of the common communications carriers industry.

Optical-Correlator Neural Network Based On Neocognitron

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin; Stoner, William W.

1994-01-01

Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

Solar power satellite system definition study. Volume 1: Executive summary

NASA Technical Reports Server (NTRS)

1979-01-01

Configuration concepts, option sizes, and systems definitions study design evolutions are reviewed. The main features of the present reference design silicon solar cell solar power satellite are described, as well as the provisions for space construction and support systems. The principal study accomplishments and conclusions are summarized according to the following tasks: (1) baseline critique; (2) construction and maintenance; (3) industrial complex needs, cost estimates, and production capacity; (4) launch complex requirements at KSC or at an offshore facility; (5) integration of the SPS/ground power network; (6) technology advancement and development; (7) costs and schedules; and (8) exploratory technology: laser annealing of solar cells degraded by proton irradiation, and a fiber-optic phase distribution link at 980 MHz.

Applying deep learning technology to automatically identify metaphase chromosomes using scanning microscopic images: an initial investigation

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Lu, Xianglan; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Li, Shibo; Liu, Hong; Zheng, Bin

2016-03-01

Automated high throughput scanning microscopy is a fast developing screening technology used in cytogenetic laboratories for the diagnosis of leukemia or other genetic diseases. However, one of the major challenges of using this new technology is how to efficiently detect the analyzable metaphase chromosomes during the scanning process. The purpose of this investigation is to develop a computer aided detection (CAD) scheme based on deep learning technology, which can identify the metaphase chromosomes with high accuracy. The CAD scheme includes an eight layer neural network. The first six layers compose of an automatic feature extraction module, which has an architecture of three convolution-max-pooling layer pairs. The 1st, 2nd and 3rd pair contains 30, 20, 20 feature maps, respectively. The seventh and eighth layers compose of a multiple layer perception (MLP) based classifier, which is used to identify the analyzable metaphase chromosomes. The performance of new CAD scheme was assessed by receiver operation characteristic (ROC) method. A number of 150 regions of interest (ROIs) were selected to test the performance of our new CAD scheme. Each ROI contains either interphase cell or metaphase chromosomes. The results indicate that new scheme is able to achieve an area under the ROC curve (AUC) of 0.886+/-0.043. This investigation demonstrates that applying a deep learning technique may enable to significantly improve the accuracy of the metaphase chromosome detection using a scanning microscopic imaging technology in the future.

An overview of 5G network slicing architecture

NASA Astrophysics Data System (ADS)

Chen, Qiang; Wang, Xiaolei; Lv, Yingying

2018-05-01

With the development of mobile communication technology, the traditional single network model has been unable to meet the needs of users, and the demand for differentiated services is increasing. In order to solve this problem, the fifth generation of mobile communication technology came into being, and as one of the key technologies of 5G, network slice is the core technology of network virtualization and software defined network, enabling network slices to flexibly provide one or more network services according to users' needs[1]. Each slice can independently tailor the network functions according to the requirements of the business scene and the traffic model and manage the layout of the corresponding network resources, to improve the flexibility of network services and the utilization of resources, and enhance the robustness and reliability of the whole network [2].

Image feature based GPS trace filtering for road network generation and road segmentation

DOE PAGES

Yuan, Jiangye; Cheriyadat, Anil M.

2015-10-19

We propose a new method to infer road networks from GPS trace data and accurately segment road regions in high-resolution aerial images. Unlike previous efforts that rely on GPS traces alone, we exploit image features to infer road networks from noisy trace data. The inferred road network is used to guide road segmentation. We show that the number of image segments spanned by the traces and the trace orientation validated with image features are important attributes for identifying GPS traces on road regions. Based on filtered traces , we construct road networks and integrate them with image features to segmentmore » road regions. Lastly, our experiments show that the proposed method produces more accurate road networks than the leading method that uses GPS traces alone, and also achieves high accuracy in segmenting road regions even with very noisy GPS data.« less

Image feature based GPS trace filtering for road network generation and road segmentation

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

Yuan, Jiangye; Cheriyadat, Anil M.

We propose a new method to infer road networks from GPS trace data and accurately segment road regions in high-resolution aerial images. Unlike previous efforts that rely on GPS traces alone, we exploit image features to infer road networks from noisy trace data. The inferred road network is used to guide road segmentation. We show that the number of image segments spanned by the traces and the trace orientation validated with image features are important attributes for identifying GPS traces on road regions. Based on filtered traces , we construct road networks and integrate them with image features to segmentmore » road regions. Lastly, our experiments show that the proposed method produces more accurate road networks than the leading method that uses GPS traces alone, and also achieves high accuracy in segmenting road regions even with very noisy GPS data.« less

Research on the transfer learning of the vehicle logo recognition

NASA Astrophysics Data System (ADS)

Zhao, Wei

2017-08-01

The Convolutional Neural Network of Deep Learning has been a huge success in the field of image intelligent transportation system can effectively solve the traffic safety, congestion, vehicle management and other problems of traffic in the city. Vehicle identification is a vital part of intelligent transportation, and the effective information in vehicles is of great significance to vehicle identification. With the traffic system on the vehicle identification technology requirements are getting higher and higher, the vehicle as an important type of vehicle information, because it should not be removed, difficult to change and other features for vehicle identification provides an important method. The current vehicle identification recognition (VLR) is mostly used to extract the characteristics of the method of classification, which for complex classification of its generalization ability to be some constraints, if the use of depth learning technology, you need a lot of training samples. In this paper, the method of convolution neural network based on transfer learning can solve this problem effectively, and it has important practical application value in the task of vehicle mark recognition.

Smart homes - current features and future perspectives.

PubMed

Chan, Marie; Campo, Eric; Estève, Daniel; Fourniols, Jean-Yves

2009-10-20

In an ageing world, maintaining good health and independence for as long as possible is essential. Instead of hospitalization or institutionalization, the elderly and disabled can be assisted in their own environment 24h a day with numerous 'smart' devices. The concept of the smart home is a promising and cost-effective way of improving home care for the elderly and the disabled in a non-obtrusive way, allowing greater independence, maintaining good health and preventing social isolation. Smart homes are equipped with sensors, actuators, and/or biomedical monitors. The devices operate in a network connected to a remote centre for data collection and processing. The remote centre diagnoses the ongoing situation and initiates assistance procedures as required. The technology can be extended to wearable and in vivo implantable devices to monitor people 24h a day both inside and outside the house. This review describes a selection of projects in developed countries on smart homes examining the various technologies available. Advantages and disadvantages, as well as the impact on modern society, are discussed. Finally, future perspectives on smart homes as part of a home-based health care network are presented.

Convolutional neural network using generated data for SAR ATR with limited samples

NASA Astrophysics Data System (ADS)

Cong, Longjian; Gao, Lei; Zhang, Hui; Sun, Peng

2018-03-01

Being able to adapt all weather at all times, it has been a hot research topic that using Synthetic Aperture Radar(SAR) for remote sensing. Despite all the well-known advantages of SAR, it is hard to extract features because of its unique imaging methodology, and this challenge attracts the research interest of traditional Automatic Target Recognition(ATR) methods. With the development of deep learning technologies, convolutional neural networks(CNNs) give us another way out to detect and recognize targets, when a huge number of samples are available, but this premise is often not hold, when it comes to monitoring a specific type of ships. In this paper, we propose a method to enhance the performance of Faster R-CNN with limited samples to detect and recognize ships in SAR images.

Preliminary research on the identification system for anthracnose and powdery mildew of sandalwood leaf based on image processing

PubMed Central

Wang, Xuefeng

2017-01-01

This paper presents a survey on a system that uses digital image processing techniques to identify anthracnose and powdery mildew diseases of sandalwood from digital images. Our main objective is researching the most suitable identification technology for the anthracnose and powdery mildew diseases of the sandalwood leaf, which provides algorithmic support for the real-time machine judgment of the health status and disease level of sandalwood. We conducted real-time monitoring of Hainan sandalwood leaves with varying severity levels of anthracnose and powdery mildew beginning in March 2014. We used image segmentation, feature extraction and digital image classification and recognition technology to carry out a comparative experimental study for the image analysis of powdery mildew, anthracnose disease and healthy leaves in the field. Performing the actual test for a large number of diseased leaves pointed to three conclusions: (1) Distinguishing effects of BP (Back Propagation) neural network method, in all kinds of classical methods, for sandalwood leaf anthracnose and powdery mildew disease are relatively good; the size of the lesion areas were closest to the actual. (2) The differences between two diseases can be shown well by the shape feature, color feature and texture feature of the disease image. (3) Identifying and diagnosing the diseased leaves have ideal results by SVM, which is based on radial basis kernel function. The identification rate of the anthracnose and healthy leaves was 92% respectively, and that of powdery mildew was 84%. Disease identification technology lays the foundation for remote monitoring disease diagnosis, preparing for remote transmission of the disease images, which is a very good guide and reference for further research of the disease identification and diagnosis system in sandalwood and other species of trees. PMID:28749977

Preliminary research on the identification system for anthracnose and powdery mildew of sandalwood leaf based on image processing.

PubMed

Wu, Chunyan; Wang, Xuefeng

2017-01-01

This paper presents a survey on a system that uses digital image processing techniques to identify anthracnose and powdery mildew diseases of sandalwood from digital images. Our main objective is researching the most suitable identification technology for the anthracnose and powdery mildew diseases of the sandalwood leaf, which provides algorithmic support for the real-time machine judgment of the health status and disease level of sandalwood. We conducted real-time monitoring of Hainan sandalwood leaves with varying severity levels of anthracnose and powdery mildew beginning in March 2014. We used image segmentation, feature extraction and digital image classification and recognition technology to carry out a comparative experimental study for the image analysis of powdery mildew, anthracnose disease and healthy leaves in the field. Performing the actual test for a large number of diseased leaves pointed to three conclusions: (1) Distinguishing effects of BP (Back Propagation) neural network method, in all kinds of classical methods, for sandalwood leaf anthracnose and powdery mildew disease are relatively good; the size of the lesion areas were closest to the actual. (2) The differences between two diseases can be shown well by the shape feature, color feature and texture feature of the disease image. (3) Identifying and diagnosing the diseased leaves have ideal results by SVM, which is based on radial basis kernel function. The identification rate of the anthracnose and healthy leaves was 92% respectively, and that of powdery mildew was 84%. Disease identification technology lays the foundation for remote monitoring disease diagnosis, preparing for remote transmission of the disease images, which is a very good guide and reference for further research of the disease identification and diagnosis system in sandalwood and other species of trees.

Seismic signal auto-detecing from different features by using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Huang, Y.; Zhou, Y.; Yue, H.; Zhou, S.

2017-12-01

We try Convolutional Neural Network to detect some features of seismic data and compare their efficience. The features include whether a signal is seismic signal or noise and the arrival time of P and S phase and each feature correspond to a Convolutional Neural Network. We first use traditional STA/LTA to recongnize some events and then use templete matching to find more events as training set for the Neural Network. To make the training set more various, we add some noise to the seismic data and make some synthetic seismic data and noise. The 3-component raw signal and time-frequancy ananlyze are used as the input data for our neural network. Our Training is performed on GPUs to achieve efficient convergence. Our method improved the precision in comparison with STA/LTA and template matching. We will move to recurrent neural network to see if this kind network is better in detect P and S phase.

Estimating standard errors in feature network models.

PubMed

Frank, Laurence E; Heiser, Willem J

2007-05-01

Feature network models are graphical structures that represent proximity data in a discrete space while using the same formalism that is the basis of least squares methods employed in multidimensional scaling. Existing methods to derive a network model from empirical data only give the best-fitting network and yield no standard errors for the parameter estimates. The additivity properties of networks make it possible to consider the model as a univariate (multiple) linear regression problem with positivity restrictions on the parameters. In the present study, both theoretical and empirical standard errors are obtained for the constrained regression parameters of a network model with known features. The performance of both types of standard error is evaluated using Monte Carlo techniques.

Research on key technology of space laser communication network

NASA Astrophysics Data System (ADS)

Chang, Chengwu; Huang, Huiming; Liu, Hongyang; Gao, Shenghua; Cheng, Liyu

2016-10-01

Since the 21st century, Spatial laser communication has made a breakthrough development. Europe, the United States, Japan and other space powers have carried out the test of spatial laser communication technology on-orbit, and put forward a series of plans. In 2011, China made the first technology demonstration of satellite-ground laser communication carried by HY-2 satellite. Nowadays, in order to improve the transmission rate of spatial network, the topic of spatial laser communication network is becoming a research hotspot at home and abroad. This thesis, from the basic problem of spatial laser communication network to solve, analyzes the main difference between spatial network and ground network, which draws forth the key technology of spatial laser communication backbone network, and systematically introduces our research on aggregation, addressing, architecture of spatial network. From the perspective of technology development status and trends, the thesis proposes the development route of spatial laser communication network in stages. So as to provide reference about the development of spatial laser communication network in China.

Coded Cooperation for Multiway Relaying in Wireless Sensor Networks †

PubMed Central

Si, Zhongwei; Ma, Junyang; Thobaben, Ragnar

2015-01-01

Wireless sensor networks have been considered as an enabling technology for constructing smart cities. One important feature of wireless sensor networks is that the sensor nodes collaborate in some manner for communications. In this manuscript, we focus on the model of multiway relaying with full data exchange where each user wants to transmit and receive data to and from all other users in the network. We derive the capacity region for this specific model and propose a coding strategy through coset encoding. To obtain good performance with practical codes, we choose spatially-coupled LDPC (SC-LDPC) codes for the coded cooperation. In particular, for the message broadcasting from the relay, we construct multi-edge-type (MET) SC-LDPC codes by repeatedly applying coset encoding. Due to the capacity-achieving property of the SC-LDPC codes, we prove that the capacity region can theoretically be achieved by the proposed MET SC-LDPC codes. Numerical results with finite node degrees are provided, which show that the achievable rates approach the boundary of the capacity region in both binary erasure channels and additive white Gaussian channels. PMID:26131675

An Analysis of Database Replication Technologies with Regard to Deep Space Network Application Requirements

NASA Technical Reports Server (NTRS)

Connell, Andrea M.

2011-01-01

The Deep Space Network (DSN) has three communication facilities which handle telemetry, commands, and other data relating to spacecraft missions. The network requires these three sites to share data with each other and with the Jet Propulsion Laboratory for processing and distribution. Many database management systems have replication capabilities built in, which means that data updates made at one location will be automatically propagated to other locations. This project examines multiple replication solutions, looking for stability, automation, flexibility, performance, and cost. After comparing these features, Oracle Streams is chosen for closer analysis. Two Streams environments are configured - one with a Master/Slave architecture, in which a single server is the source for all data updates, and the second with a Multi-Master architecture, in which updates originating from any of the servers will be propagated to all of the others. These environments are tested for data type support, conflict resolution, performance, changes to the data structure, and behavior during and after network or server outages. Through this experimentation, it is determined which requirements of the DSN can be met by Oracle Streams and which cannot.

Coded Cooperation for Multiway Relaying in Wireless Sensor Networks.

PubMed

Si, Zhongwei; Ma, Junyang; Thobaben, Ragnar

2015-06-29

Wireless sensor networks have been considered as an enabling technology for constructing smart cities. One important feature of wireless sensor networks is that the sensor nodes collaborate in some manner for communications. In this manuscript, we focus on the model of multiway relaying with full data exchange where each user wants to transmit and receive data to and from all other users in the network. We derive the capacity region for this specific model and propose a coding strategy through coset encoding. To obtain good performance with practical codes, we choose spatially-coupled LDPC (SC-LDPC) codes for the coded cooperation. In particular, for the message broadcasting from the relay, we construct multi-edge-type (MET) SC-LDPC codes by repeatedly applying coset encoding. Due to the capacity-achieving property of the SC-LDPC codes, we prove that the capacity region can theoretically be achieved by the proposed MET SC-LDPC codes. Numerical results with finite node degrees are provided, which show that the achievable rates approach the boundary of the capacity region in both binary erasure channels and additive white Gaussian channels.

Plans & Policies for Technology in Education: A Compendium. A Technology Leadership Network Special Report.

ERIC Educational Resources Information Center

National School Boards Association, Alexandria, VA. Inst. for the Transfer of Technology to Education.

This document shows how education leaders nationwide--many of them part of the National School Boards Association's 345-district Technology Leadership Network--have addressed technology-related policy issues such as copyright, purchasing, network/Internet use, and ethics as well as technology planning topics including staff development, classroom…

The Device Centric Communication System for 5G Networks

NASA Astrophysics Data System (ADS)

Biswash, S. K.; Jayakody, D. N. K.

2017-01-01

The Fifth Generation Communication (5G) networks have several functional features such as: Massive Multiple Input and Multiple Output (MIMO), Device centric data and voice support, Smarter-device communications, etc. The objective for 5G networks is to gain the 1000x more throughput, 10x spectral efficiency, 100 x more energy efficiency than existing technologies. The 5G system will provide the balance between the Quality of Experience (QoE) and the Quality of Service (QoS), without compromising the user benefit. The data rate has been the key metric for wireless QoS; QoE deals with the delay and throughput. In order to realize a balance between the QoS and QoE, we propose a cellular Device centric communication methodology for the overlapping network coverage area in the 5G communication system. The multiple beacon signals mobile tower refers to an overlapping network area, and a user must be forwarded to the next location area. To resolve this issue, we suggest the user centric methodology (without Base Station interface) to handover the device in the next area, until the users finalize the communication. The proposed method will reduce the signalling cost and overheads for the communication.

A High Performance VLSI Computer Architecture For Computer Graphics

NASA Astrophysics Data System (ADS)

Chin, Chi-Yuan; Lin, Wen-Tai

1988-10-01

A VLSI computer architecture, consisting of multiple processors, is presented in this paper to satisfy the modern computer graphics demands, e.g. high resolution, realistic animation, real-time display etc.. All processors share a global memory which are partitioned into multiple banks. Through a crossbar network, data from one memory bank can be broadcasted to many processors. Processors are physically interconnected through a hyper-crossbar network (a crossbar-like network). By programming the network, the topology of communication links among processors can be reconfigurated to satisfy specific dataflows of different applications. Each processor consists of a controller, arithmetic operators, local memory, a local crossbar network, and I/O ports to communicate with other processors, memory banks, and a system controller. Operations in each processor are characterized into two modes, i.e. object domain and space domain, to fully utilize the data-independency characteristics of graphics processing. Special graphics features such as 3D-to-2D conversion, shadow generation, texturing, and reflection, can be easily handled. With the current high density interconnection (MI) technology, it is feasible to implement a 64-processor system to achieve 2.5 billion operations per second, a performance needed in most advanced graphics applications.

A Survey on Mobility Support in Wireless Body Area Networks

PubMed Central

Kim, Beom-Su; Kim, Kyong Hoon; Kim, Ki-Il

2017-01-01

Wireless Body Area Networks (WBANs) have attracted research interests from the community, as more promising healthcare applications have a tendency to employ them as underlying network technology. While taking design issues, such as small size hardware as well as low power computing, into account, a lot of research has been proposed to accomplish the given tasks in WBAN. However, since most of the existing works are basically developed by assuming all nodes in the static state, these schemes therefore cannot be applied in real scenarios where network topology between sensor nodes changes frequently and unexpectedly according to human moving behavior. However, as far as the authors know, there is no survey paper to focus on research challenges for mobility support in WBAN yet. To address this deficiency, in this paper, we present the state-of-the-art approaches and discuss the important features of related to mobility in WBAN. We give an overview of mobility model and categorize the models as individual and group. Furthermore, an overview of networking techniques in the recent literature and summary are compiled for comparison in several aspects. The article also suggests potential directions for future research in the field. PMID:28387745

Implementation of a Synchronized Oscillator Circuit for Fast Sensing and Labeling of Image Objects

PubMed Central

Kowalski, Jacek; Strzelecki, Michal; Kim, Hyongsuk

2011-01-01

We present an application-specific integrated circuit (ASIC) CMOS chip that implements a synchronized oscillator cellular neural network with a matrix size of 32 × 32 for object sensing and labeling in binary images. Networks of synchronized oscillators are a recently developed tool for image segmentation and analysis. Its parallel network operation is based on a “temporary correlation” theory that attempts to describe scene recognition as if performed by the human brain. The synchronized oscillations of neuron groups attract a person’s attention if he or she is focused on a coherent stimulus (image object). For more than one perceived stimulus, these synchronized patterns switch in time between different neuron groups, thus forming temporal maps that code several features of the analyzed scene. In this paper, a new oscillator circuit based on a mathematical model is proposed, and the network architecture and chip functional blocks are presented and discussed. The proposed chip is implemented in AMIS 0.35 μm C035M-D 5M/1P technology. An application of the proposed network chip for the segmentation of insulin-producing pancreatic islets in magnetic resonance liver images is presented. PMID:22163803

A Survey on Mobility Support in Wireless Body Area Networks.

PubMed

Kim, Beom-Su; Kim, Kyong Hoon; Kim, Ki-Il

2017-04-07

Wireless Body Area Networks (WBANs) have attracted research interests from the community, as more promising healthcare applications have a tendency to employ them as underlying network technology. While taking design issues, such as small size hardware as well as low power computing, into account, a lot of research has been proposed to accomplish the given tasks in WBAN. However, since most of the existing works are basically developed by assuming all nodes in the static state, these schemes therefore cannot be applied in real scenarios where network topology between sensor nodes changes frequently and unexpectedly according to human moving behavior. However, as far as the authors know, there is no survey paper to focus on research challenges for mobility support in WBAN yet. To address this deficiency, in this paper, we present the state-of-the-art approaches and discuss the important features of related to mobility in WBAN. We give an overview of mobility model and categorize the models as individual and group. Furthermore, an overview of networking techniques in the recent literature and summary are compiled for comparison in several aspects. The article also suggests potential directions for future research in the field.

Defect detection and classification of galvanized stamping parts based on fully convolution neural network

NASA Astrophysics Data System (ADS)

Xiao, Zhitao; Leng, Yanyi; Geng, Lei; Xi, Jiangtao

2018-04-01

In this paper, a new convolution neural network method is proposed for the inspection and classification of galvanized stamping parts. Firstly, all workpieces are divided into normal and defective by image processing, and then the defective workpieces extracted from the region of interest (ROI) area are input to the trained fully convolutional networks (FCN). The network utilizes an end-to-end and pixel-to-pixel training convolution network that is currently the most advanced technology in semantic segmentation, predicts result of each pixel. Secondly, we mark the different pixel values of the workpiece, defect and background for the training image, and use the pixel value and the number of pixels to realize the recognition of the defects of the output picture. Finally, the defect area's threshold depended on the needs of the project is set to achieve the specific classification of the workpiece. The experiment results show that the proposed method can successfully achieve defect detection and classification of galvanized stamping parts under ordinary camera and illumination conditions, and its accuracy can reach 99.6%. Moreover, it overcomes the problem of complex image preprocessing and difficult feature extraction and performs better adaptability.

75 FR 55360 - Networking and Information Technology Research and Development (NITRD) Program: Draft NITRD 2010...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-10

... NATIONAL SCIENCE FOUNDATION Networking and Information Technology Research and Development (NITRD... Information Technology Research and Development (NITRD). ACTION: Notice, request for public comment. FOR..., the National Coordination Office for Networking and Information Technology Research and Development...

Robust Learning of High-dimensional Biological Networks with Bayesian Networks

NASA Astrophysics Data System (ADS)

Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.

Improving deep convolutional neural networks with mixed maxout units.

PubMed

Zhao, Hui-Zhen; Liu, Fu-Xian; Li, Long-Yue

2017-01-01

Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN) model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance.

Prediction of interface residue based on the features of residue interaction network.

PubMed

Jiao, Xiong; Ranganathan, Shoba

2017-11-07

Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model. Copyright © 2017 Elsevier Ltd. All rights reserved.

Autoscoring Essays Based on Complex Networks

ERIC Educational Resources Information Center

Ke, Xiaohua; Zeng, Yongqiang; Luo, Haijiao

2016-01-01

This article presents a novel method, the Complex Dynamics Essay Scorer (CDES), for automated essay scoring using complex network features. Texts produced by college students in China were represented as scale-free networks (e.g., a word adjacency model) from which typical network features, such as the in-/out-degrees, clustering coefficient (CC),…

Distributed intelligent control and status networking

NASA Technical Reports Server (NTRS)

Fortin, Andre; Patel, Manoj

1993-01-01

Over the past two years, the Network Control Systems Branch (Code 532) has been investigating control and status networking technologies. These emerging technologies use distributed processing over a network to accomplish a particular custom task. These networks consist of small intelligent 'nodes' that perform simple tasks. Containing simple, inexpensive hardware and software, these nodes can be easily developed and maintained. Once networked, the nodes can perform a complex operation without a central host. This type of system provides an alternative to more complex control and status systems which require a central computer. This paper will provide some background and discuss some applications of this technology. It will also demonstrate the suitability of one particular technology for the Space Network (SN) and discuss the prototyping activities of Code 532 utilizing this technology.

Deep Spatial-Temporal Joint Feature Representation for Video Object Detection.

PubMed

Zhao, Baojun; Zhao, Boya; Tang, Linbo; Han, Yuqi; Wang, Wenzheng

2018-03-04

With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP).

Technology acceptance perception for promotion of sustainable consumption.

PubMed

Biswas, Aindrila; Roy, Mousumi

2018-03-01

Economic growth in the past decades has resulted in change in consumption pattern and emergence of tech-savvy generation with unprecedented increase in the usage of social network technology. In this paper, the technology acceptance value gap adapted from the technology acceptance model has been applied as a tool supporting social network technology usage and subsequent promotion of sustainable consumption. The data generated through the use of structured questionnaires have been analyzed using structural equation modeling. The validity of the model and path estimates signifies the robustness of Technology Acceptance value gap in adjudicating the efficiency of social network technology usage in augmentation of sustainable consumption and awareness. The results indicate that subjective norm gap, ease-of-operation gap, and quality of green information gap have the most adversarial impact on social network technology usage. Eventually social networking technology usage has been identified as a significant antecedent of sustainable consumption.

ANALYSIS OF CLINICAL AND DERMOSCOPIC FEATURES FOR BASAL CELL CARCINOMA NEURAL NETWORK CLASSIFICATION

PubMed Central

Cheng, Beibei; Stanley, R. Joe; Stoecker, William V; Stricklin, Sherea M.; Hinton, Kristen A.; Nguyen, Thanh K.; Rader, Ryan K.; Rabinovitz, Harold S.; Oliviero, Margaret; Moss, Randy H.

2012-01-01

Background Basal cell carcinoma (BCC) is the most commonly diagnosed cancer in the United States. In this research, we examine four different feature categories used for diagnostic decisions, including patient personal profile (patient age, gender, etc.), general exam (lesion size and location), common dermoscopic (blue-gray ovoids, leaf-structure dirt trails, etc.), and specific dermoscopic lesion (white/pink areas, semitranslucency, etc.). Specific dermoscopic features are more restricted versions of the common dermoscopic features. Methods Combinations of the four feature categories are analyzed over a data set of 700 lesions, with 350 BCCs and 350 benign lesions, for lesion discrimination using neural network-based techniques, including Evolving Artificial Neural Networks and Evolving Artificial Neural Network Ensembles. Results Experiment results based on ten-fold cross validation for training and testing the different neural network-based techniques yielded an area under the receiver operating characteristic curve as high as 0.981 when all features were combined. The common dermoscopic lesion features generally yielded higher discrimination results than other individual feature categories. Conclusions Experimental results show that combining clinical and image information provides enhanced lesion discrimination capability over either information source separately. This research highlights the potential of data fusion as a model for the diagnostic process. PMID:22724561

Enabling Communication and Navigation Technologies for Future Near Earth Science Missions

NASA Technical Reports Server (NTRS)

Israel, David J.; Heckler, Gregory; Menrad, Robert; Hudiburg, John; Boroson, Don; Robinson, Bryan; Cornwell, Donald

2016-01-01

In 2015, the Earth Regimes Network Evolution Study (ERNESt) proposed an architectural concept and technologies that evolve to enable space science and exploration missions out to the 2040 timeframe. The architectural concept evolves the current instantiations of the Near Earth Network and Space Network with new technologies to provide a global communication and navigation network that provides communication and navigation services to a wide range of space users in the near Earth domain. The technologies included High Rate Optical Communications, Optical Multiple Access (OMA), Delay Tolerant Networking (DTN), User Initiated Services (UIS), and advanced Position, Navigation, and Timing technology. This paper describes the key technologies and their current technology readiness levels. Examples of science missions that could be enabled by the technologies and the projected operational benefits of the architecture concept to missions are also described.

Can clinical use of Social Media improve quality of care in mental Health? A Health Technology Assessment approach in an Italian mental health service.

PubMed

Di Napoli, Wilma Angela; Nollo, Giandomenico; Pace, Nicola; Torri, Emanuele

2015-09-01

Clinical use of modern Information and Communication Technologies such as Social Media (SM) can easily reach and empower groups of population at risk or affected by chronic diseases, and promote improvement of quality of care. In the paper we present an assessment of SM (i.e. e-mails, websites, on line social networks, apps) in the management of mental disorders, carried out in the Mental Health Service of Trento (Italy) according to Health Technology Assessment criteria. A systematic review of literature was performed to evaluate technical features, safety and effectiveness of SM. To understand usage rate and attitude towards new social technologies of patients and professionals, we performed a context analysis by a survey conducted over a group of 88 psychiatric patients and a group of 35 professionals. At last, we made recommendations for decision makers in order to promote SM for the management of mental disorders in a context of prioritization of investments in health care.

Why Aren't Our Digital Solutions Working for Everyone?

PubMed

Winkle, Brian Van; Carpenter, Neil; Moscucci, Mauro

2017-11-01

The article explores a digital injustice that is occurring across the country: that digital solutions intended to increase health care access and quality often neglect those that need them most. It further shows that when it comes to digital innovation, health care professionals and technology companies rarely have any incentives to focus on underserved populations. Nevertheless, we argue that the technologies that are leaving these communities behind are the same ones that can best support them. The key is in leveraging these technologies with: (a) design features that accommodate various levels of technological proficiency (e-literacy), (b) tech-enabled community health workers and navigators who can function as liaisons between patients and clinicians, and (c) analytics and customer relationship management tools that enable health care professionals and support networks to provide the right interventions to the right patients. Finally, we argue that community health care workers will need to be incentivized to play a larger role in building and adopting innovations targeting the underserved. © 2017 American Medical Association. All Rights Reserved.

Hybrid network defense model based on fuzzy evaluation.

PubMed

Cho, Ying-Chiang; Pan, Jen-Yi

2014-01-01

With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network's existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter's inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture.

Convergence

NASA Astrophysics Data System (ADS)

Darcie, Thomas E.; Doverspike, Robert; Zirngibl, Martin; Korotky, Steven K.

2005-02-01

Call for Papers: Convergence Convergence has become a popular theme in telecommunications, one that has broad implications across all segments of the industry. Continual evolution of technology and applications continues to erase lines between traditionally separate lines of business, with dramatic consequences for vendors, service providers, and consumers. Spectacular advances in all layers of optical networking-leading to abundant, dynamic, cost-effective, and reliable wide-area and local-area connections-have been essential drivers of this evolution. As services and networks continue to evolve towards some notion of convergence, the continued role of optical networks must be explored. One vision of convergence renders all information in a common packet (especially IP) format. This vision is driven by the proliferation of data services. For example, time-division multiplexed (TDM) voice becomes VoIP. Analog cable-television signals become MPEG bits streamed to digital set-top boxes. T1 or OC-N private lines migrate to Ethernet virtual private networks (VPNs). All these packets coexist peacefully within a single packet-routing methodology built on an optical transport layer that combines the flexibility and cost of data networks with telecom-grade reliability. While this vision is appealing in its simplicity and shared widely, specifics of implementation raise many challenges and differences of opinion. For example, many seek to expand the role of Ethernet in these transport networks, while massive efforts are underway to make traditional TDM networks more data friendly within an evolved but backward-compatible SDH/SONET (synchronous digital hierarchy and synchronous optical network) multiplexing hierarchy. From this common underlying theme follow many specific instantiations. Examples include the convergence at the physical, logical, and operational levels of voice and data, video and data, private-line and virtual private-line, fixed and mobile, and local and long-haul services. These trends have many consequences for consumers, vendors, and carriers. Faced with large volumes of low-margin data traffic mixed with traditional voice services, the need for capital conservation and operational efficiency drives carriers away from today's separate overlay networks for each service and towards "converged" platforms. For example, cable operators require transport of multiple services over both hybrid fiber coax (HFC) and DWDM transport technologies. Local carriers seek an economical architecture to deliver integrated services on optically enabled broadband-access networks. Services over wireless-access networks must coexist with those from wired networks. In each case, convergence of networks and services inspires an important set of questions and challenges, driven by the need for low cost, operational efficiency, service performance requirements, and optical transport technology options. This Feature Issue explores the various interpretations and implications of network convergence pertinent to optical networking. How does convergence affect the evolution of optical transport-layer and control approaches? Are the implied directions consistent with research vision for optical networks? Substantial challenges remain. Papers are solicited across the broad spectrum of interests. These include, but are not limited to:

Convergence

NASA Astrophysics Data System (ADS)

Darcie, Thomas E.; Doverspike, Robert; Zirngibl, Martin; Korotky, Steven K.

2005-03-01

Call for Papers: Convergence Convergence has become a popular theme in telecommunications, one that has broad implications across all segments of the industry. Continual evolution of technology and applications continues to erase lines between traditionally separate lines of business, with dramatic consequences for vendors, service providers, and consumers. Spectacular advances in all layers of optical networking-leading to abundant, dynamic, cost-effective, and reliable wide-area and local-area connections-have been essential drivers of this evolution. As services and networks continue to evolve towards some notion of convergence, the continued role of optical networks must be explored. One vision of convergence renders all information in a common packet (especially IP) format. This vision is driven by the proliferation of data services. For example, time-division multiplexed (TDM) voice becomes VoIP. Analog cable-television signals become MPEG bits streamed to digital set-top boxes. T1 or OC-N private lines migrate to Ethernet virtual private networks (VPNs). All these packets coexist peacefully within a single packet-routing methodology built on an optical transport layer that combines the flexibility and cost of data networks with telecom-grade reliability. While this vision is appealing in its simplicity and shared widely, specifics of implementation raise many challenges and differences of opinion. For example, many seek to expand the role of Ethernet in these transport networks, while massive efforts are underway to make traditional TDM networks more data friendly within an evolved but backward-compatible SDH/SONET (synchronous digital hierarchy and synchronous optical network) multiplexing hierarchy. From this common underlying theme follow many specific instantiations. Examples include the convergence at the physical, logical, and operational levels of voice and data, video and data, private-line and virtual private-line, fixed and mobile, and local and long-haul services. These trends have many consequences for consumers, vendors, and carriers. Faced with large volumes of low-margin data traffic mixed with traditional voice services, the need for capital conservation and operational efficiency drives carriers away from today's separate overlay networks for each service and towards "converged" platforms. For example, cable operators require transport of multiple services over both hybrid fiber coax (HFC) and DWDM transport technologies. Local carriers seek an economical architecture to deliver integrated services on optically enabled broadband-access networks. Services over wireless-access networks must coexist with those from wired networks. In each case, convergence of networks and services inspires an important set of questions and challenges, driven by the need for low cost, operational efficiency, service performance requirements, and optical transport technology options. This Feature Issue explores the various interpretations and implications of network convergence pertinent to optical networking. How does convergence affect the evolution of optical transport-layer and control approaches? Are the implied directions consistent with research vision for optical networks? Substantial challenges remain. Papers are solicited across the broad spectrum of interests. These include, but are not limited to:

NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

PubMed

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available.

Traffic-aware energy saving scheme with modularization supporting in TWDM-PON

NASA Astrophysics Data System (ADS)

Xiong, Yu; Sun, Peng; Liu, Chuanbo; Guan, Jianjun

2017-01-01

Time and wavelength division multiplexed passive optical network (TWDM-PON) is considered to be a primary solution for next-generation passive optical network stage 2 (NG-PON2). Due to the feature of multi-wavelength transmission of TWDM-PON, some of the transmitters/receivers at the optical line terminal (OLT) could be shut down to reduce the energy consumption. Therefore, a novel scheme called traffic-aware energy saving scheme with modularization supporting is proposed. Through establishing the modular energy consumption model of OLT, the wavelength transmitters/receivers at OLT could be switched on or shut down adaptively depending on sensing the status of network traffic load, thus the energy consumption of OLT will be effectively reduced. Furthermore, exploring the technology of optical network unit (ONU) modularization, each module of ONU could be switched to sleep or active mode independently in order to reduce the energy consumption of ONU. Simultaneously, the polling sequence of ONU could be changed dynamically via sensing the packet arrival time. In order to guarantee the delay performance of network traffic, the sub-cycle division strategy is designed to transmit the real-time traffic preferentially. Finally, simulation results verify that the proposed scheme is able to reduce the energy consumption of the network while maintaining the traffic delay performance.

The Geospatial Characteristics of a Social Movement Communication Network

PubMed Central

Conover, Michael D.; Davis, Clayton; Ferrara, Emilio; McKelvey, Karissa; Menczer, Filippo; Flammini, Alessandro

2013-01-01

Social movements rely in large measure on networked communication technologies to organize and disseminate information relating to the movements’ objectives. In this work we seek to understand how the goals and needs of a protest movement are reflected in the geographic patterns of its communication network, and how these patterns differ from those of stable political communication. To this end, we examine an online communication network reconstructed from over 600,000 tweets from a thirty-six week period covering the birth and maturation of the American anticapitalist movement, Occupy Wall Street. We find that, compared to a network of stable domestic political communication, the Occupy Wall Street network exhibits higher levels of locality and a hub and spoke structure, in which the majority of non-local attention is allocated to high-profile locations such as New York, California, and Washington D.C. Moreover, we observe that information flows across state boundaries are more likely to contain framing language and references to the media, while communication among individuals in the same state is more likely to reference protest action and specific places and times. Tying these results to social movement theory, we propose that these features reflect the movement’s efforts to mobilize resources at the local level and to develop narrative frames that reinforce collective purpose at the national level. PMID:23483885

The geospatial characteristics of a social movement communication network.

PubMed

Conover, Michael D; Davis, Clayton; Ferrara, Emilio; McKelvey, Karissa; Menczer, Filippo; Flammini, Alessandro

2013-01-01

Social movements rely in large measure on networked communication technologies to organize and disseminate information relating to the movements' objectives. In this work we seek to understand how the goals and needs of a protest movement are reflected in the geographic patterns of its communication network, and how these patterns differ from those of stable political communication. To this end, we examine an online communication network reconstructed from over 600,000 tweets from a thirty-six week period covering the birth and maturation of the American anticapitalist movement, Occupy Wall Street. We find that, compared to a network of stable domestic political communication, the Occupy Wall Street network exhibits higher levels of locality and a hub and spoke structure, in which the majority of non-local attention is allocated to high-profile locations such as New York, California, and Washington D.C. Moreover, we observe that information flows across state boundaries are more likely to contain framing language and references to the media, while communication among individuals in the same state is more likely to reference protest action and specific places and times. Tying these results to social movement theory, we propose that these features reflect the movement's efforts to mobilize resources at the local level and to develop narrative frames that reinforce collective purpose at the national level.

An Evaluation of optional timing/synchronization features to support selection of an optimum design for the DCS digital communication network

NASA Technical Reports Server (NTRS)

Bradley, D. B.; Cain, J. B., III; Williard, M. W.

1978-01-01

The task was to evaluate the ability of a set of timing/synchronization subsystem features to provide a set of desirable characteristics for the evolving Defense Communications System digital communications network. The set of features related to the approaches by which timing/synchronization information could be disseminated throughout the network and the manner in which this information could be utilized to provide a synchronized network. These features, which could be utilized in a large number of different combinations, included mutual control, directed control, double ended reference links, independence of clock error measurement and correction, phase reference combining, and self organizing.

Telesat Canada's mobile satellite system

NASA Astrophysics Data System (ADS)

Bertenyi, E.; Wachira, M.

1987-10-01

Telesat Canada plans to begin instituting mobile satellite ('Msat') services in the early 1990s, in order to permit voice and data communications between land vehicles, aircraft, and ships throughout the remote northern regions and the 200-mile offshore regions of Canada and any other point on Canadian territory. An account is presently given of Msat's overall configuration and projected capacity, together with the design features and performance capabilities of the constituent ground, space, and network control segments. Key technology items are the spacecraft high power RF amplifier and its large deployable antenna.

Impact of Terrain Features for Tactical Network Connectivity

DTIC Science & Technology

2013-09-01

Impact of Terrain Features for Tactical Network Connectivity David Tate Lance Joneckis John Fregeau Corinne Kramer David Sparrow I N S T I T U...2000. I N S T I T U T E F O R D E F E N S E A N A LY S E S IDA Document NS D-5026 Impact of Terrain Features for Tactical Network Connectivity...visibility is acceptable when networks operate over flat terrain. Under our simple LOS model of connectivity, mobility has little impact in such an

Social Networking Adapted for Distributed Scientific Collaboration

NASA Technical Reports Server (NTRS)

Karimabadi, Homa

2012-01-01

Share is a social networking site with novel, specially designed feature sets to enable simultaneous remote collaboration and sharing of large data sets among scientists. The site will include not only the standard features found on popular consumer-oriented social networking sites such as Facebook and Myspace, but also a number of powerful tools to extend its functionality to a science collaboration site. A Virtual Observatory is a promising technology for making data accessible from various missions and instruments through a Web browser. Sci-Share augments services provided by Virtual Observatories by enabling distributed collaboration and sharing of downloaded and/or processed data among scientists. This will, in turn, increase science returns from NASA missions. Sci-Share also enables better utilization of NASA s high-performance computing resources by providing an easy and central mechanism to access and share large files on users space or those saved on mass storage. The most common means of remote scientific collaboration today remains the trio of e-mail for electronic communication, FTP for file sharing, and personalized Web sites for dissemination of papers and research results. Each of these tools has well-known limitations. Sci-Share transforms the social networking paradigm into a scientific collaboration environment by offering powerful tools for cooperative discourse and digital content sharing. Sci-Share differentiates itself by serving as an online repository for users digital content with the following unique features: a) Sharing of any file type, any size, from anywhere; b) Creation of projects and groups for controlled sharing; c) Module for sharing files on HPC (High Performance Computing) sites; d) Universal accessibility of staged files as embedded links on other sites (e.g. Facebook) and tools (e.g. e-mail); e) Drag-and-drop transfer of large files, replacing awkward e-mail attachments (and file size limitations); f) Enterprise-level data and messaging encryption; and g) Easy-to-use intuitive workflow.

Highly Parallel Computing Architectures by using Arrays of Quantum-dot Cellular Automata (QCA): Opportunities, Challenges, and Recent Results

NASA Technical Reports Server (NTRS)

Fijany, Amir; Toomarian, Benny N.

2000-01-01

There has been significant improvement in the performance of VLSI devices, in terms of size, power consumption, and speed, in recent years and this trend may also continue for some near future. However, it is a well known fact that there are major obstacles, i.e., physical limitation of feature size reduction and ever increasing cost of foundry, that would prevent the long term continuation of this trend. This has motivated the exploration of some fundamentally new technologies that are not dependent on the conventional feature size approach. Such technologies are expected to enable scaling to continue to the ultimate level, i.e., molecular and atomistic size. Quantum computing, quantum dot-based computing, DNA based computing, biologically inspired computing, etc., are examples of such new technologies. In particular, quantum-dots based computing by using Quantum-dot Cellular Automata (QCA) has recently been intensely investigated as a promising new technology capable of offering significant improvement over conventional VLSI in terms of reduction of feature size (and hence increase in integration level), reduction of power consumption, and increase of switching speed. Quantum dot-based computing and memory in general and QCA specifically, are intriguing to NASA due to their high packing density (10(exp 11) - 10(exp 12) per square cm ) and low power consumption (no transfer of current) and potentially higher radiation tolerant. Under Revolutionary Computing Technology (RTC) Program at the NASA/JPL Center for Integrated Space Microelectronics (CISM), we have been investigating the potential applications of QCA for the space program. To this end, exploiting the intrinsic features of QCA, we have designed novel QCA-based circuits for co-planner (i.e., single layer) and compact implementation of a class of data permutation matrices, a class of interconnection networks, and a bit-serial processor. Building upon these circuits, we have developed novel algorithms and QCA-based architectures for highly parallel and systolic computation of signal/image processing applications, such as FFT and Wavelet and Wlash-Hadamard Transforms.

Modeling inter-signal arrival times for accurate detection of CAN bus signal injection attacks

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

Moore, Michael Roy; Bridges, Robert A; Combs, Frank L

Modern vehicles rely on hundreds of on-board electronic control units (ECUs) communicating over in-vehicle networks. As external interfaces to the car control networks (such as the on-board diagnostic (OBD) port, auxiliary media ports, etc.) become common, and vehicle-to-vehicle / vehicle-to-infrastructure technology is in the near future, the attack surface for vehicles grows, exposing control networks to potentially life-critical attacks. This paper addresses the need for securing the CAN bus by detecting anomalous traffic patterns via unusual refresh rates of certain commands. While previous works have identified signal frequency as an important feature for CAN bus intrusion detection, this paper providesmore » the first such algorithm with experiments on five attack scenarios. Our data-driven anomaly detection algorithm requires only five seconds of training time (on normal data) and achieves true positive / false discovery rates of 0.9998/0.00298, respectively (micro-averaged across the five experimental tests).« less

The application of data mining techniques to oral cancer prognosis.

PubMed

Tseng, Wan-Ting; Chiang, Wei-Fan; Liu, Shyun-Yeu; Roan, Jinsheng; Lin, Chun-Nan

2015-05-01

This study adopted an integrated procedure that combines the clustering and classification features of data mining technology to determine the differences between the symptoms shown in past cases where patients died from or survived oral cancer. Two data mining tools, namely decision tree and artificial neural network, were used to analyze the historical cases of oral cancer, and their performance was compared with that of logistic regression, the popular statistical analysis tool. Both decision tree and artificial neural network models showed superiority to the traditional statistical model. However, as to clinician, the trees created by the decision tree models are relatively easier to interpret compared to that of the artificial neural network models. Cluster analysis also discovers that those stage 4 patients whose also possess the following four characteristics are having an extremely low survival rate: pN is N2b, level of RLNM is level I-III, AJCC-T is T4, and cells mutate situation (G) is moderate.

A Percolation Model for Fracking

NASA Astrophysics Data System (ADS)

Norris, J. Q.; Turcotte, D. L.; Rundle, J. B.

2014-12-01

Developments in fracking technology have enabled the recovery of vast reserves of oil and gas; yet, there is very little publicly available scientific research on fracking. Traditional reservoir simulator models for fracking are computationally expensive, and require many hours on a supercomputer to simulate a single fracking treatment. We have developed a computationally inexpensive percolation model for fracking that can be used to understand the processes and risks associated with fracking. In our model, a fluid is injected from a single site and a network of fractures grows from the single site. The fracture network grows in bursts, the failure of a relatively strong bond followed by the failure of a series of relatively weak bonds. These bursts display similarities to micro seismic events observed during a fracking treatment. The bursts follow a power-law (Gutenburg-Richter) frequency-size distribution and have growth rates similar to observed earthquake moment rates. These are quantifiable features that can be compared to observed microseismicity to help understand the relationship between observed microseismicity and the underlying fracture network.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

NASA Astrophysics Data System (ADS)

Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

Integrated Network Testbed for Energy Grid Research and Technology

Science.gov Websites

Network Testbed for Energy Grid Research and Technology Experimentation Project Under the Integrated Network Testbed for Energy Grid Research and Technology Experimentation (INTEGRATE) project, NREL and partners completed five successful technology demonstrations at the ESIF. INTEGRATE is a $6.5-million, cost

Direct Data Distribution From Low-Earth Orbit

NASA Technical Reports Server (NTRS)

Budinger, James M.; Fujikawa, Gene; Kunath, Richard R.; Nguyen, Nam T.; Romanofsky, Robert R.; Spence, Rodney L.

1997-01-01

NASA Lewis Research Center (LeRC) is developing the space and ground segment technologies necessary to demonstrate a direct data distribution (1)3) system for use in space-to-ground communication links from spacecraft in low-Earth orbit (LEO) to strategically located tracking ground terminals. The key space segment technologies include a K-band (19 GHz) MMIC-based transmit phased array antenna, and a multichannel bandwidth- and power-efficient digital encoder/modulate with an aggregate data rate of 622 Mb/s. Along with small (1.8 meter), low-cost tracking terminals on the ground, the D3 system enables affordable distribution of data to the end user or archive facility through interoperability with commercial terrestrial telecommunications networks. The D3 system is applicable to both government and commercial science and communications spacecraft in LEO. The features and benefits of the D3 system concept are described. Starting with typical orbital characteristics, a set of baseline requirements for representative applications is developed, including requirements for onboard storage and tracking terminals, and sample link budgets are presented. Characteristics of the transmit array antenna and digital encoder/modulator are described. The architecture and components of the tracking terminal are described, including technologies for the next generation terminal. Candidate flights of opportunity for risk mitigation and space demonstration of the D3 features are identified.

Classification and pose estimation of objects using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-03-01

A new nonlinear feature extraction method called the maximum representation and discrimination feature (MRDF) method is presented for extraction of features from input image data. It implements transformations similar to the Sigma-Pi neural network. However, the weights of the MRDF are obtained in closed form, and offer advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We show its use in estimating the class and pose of images of real objects and rendered solid CAD models of machine parts from single views using a feature-space trajectory (FST) neural network classifier. We show more accurate classification and pose estimation results than are achieved by standard principal component analysis (PCA) and Fukunaga-Koontz (FK) feature extraction methods.

Heterogeneous Wireless Mesh Network Technology Evaluation for Space Proximity and Surface Applications

NASA Technical Reports Server (NTRS)

DeCristofaro, Michael A.; Lansdowne, Chatwin A.; Schlesinger, Adam M.

2014-01-01

NASA has identified standardized wireless mesh networking as a key technology for future human and robotic space exploration. Wireless mesh networks enable rapid deployment, provide coverage in undeveloped regions. Mesh networks are also self-healing, resilient, and extensible, qualities not found in traditional infrastructure-based networks. Mesh networks can offer lower size, weight, and power (SWaP) than overlapped infrastructure-perapplication. To better understand the maturity, characteristics and capability of the technology, we developed an 802.11 mesh network consisting of a combination of heterogeneous commercial off-the-shelf devices and opensource firmware and software packages. Various streaming applications were operated over the mesh network, including voice and video, and performance measurements were made under different operating scenarios. During the testing several issues with the currently implemented mesh network technology were identified and outlined for future work.

Modern Workflows for Fracture Rock Hydrogeology

NASA Astrophysics Data System (ADS)

Doe, T.

2015-12-01

Discrete Fracture Network (DFN) is a numerical simulation approach that represents a conducting fracture network using geologically realistic geometries and single-conductor hydraulic and transport properties. In terms of diffusion analogues, equivalent porous media derive from heat conduction in continuous media, while DFN simulation is more similar to electrical flow and diffusion in circuits with discrete pathways. DFN modeling grew out of pioneering work of David Snow in the late 1960s with additional impetus in the 1970's from the development of the development of stochastic approaches for describing of fracture geometric and hydrologic properties. Research in underground test facilities for radioactive waste disposal developed the necessary linkages between characterization technologies and simulation as well as bringing about a hybrid deterministic stochastic approach. Over the past 40 years DFN simulation and characterization methods have moved from the research environment into practical, commercial application. The key geologic, geophysical and hydrologic tools provide the required DFN inputs of conductive fracture intensity, orientation, and transmissivity. Flow logging either using downhole tool or by detailed packer testing identifies the locations of conducting features in boreholes, and image logging provides information on the geology and geometry of the conducting features. Multi-zone monitoring systems isolate the individual conductors, and with subsequent drilling and characterization perturbations help to recognize connectivity and compartmentalization in the fracture network. Tracer tests and core analysis provide critical information on the transport properties especially matrix diffusion unidentified conducting pathways. Well test analyses incorporating flow dimension boundary effects provide further constraint on the conducting geometry of the fracture network.

BP network for atorvastatin effect evaluation from ultrasound images features classification

NASA Astrophysics Data System (ADS)

Fang, Mengjie; Yang, Xin; Liu, Yang; Xu, Hongwei; Liang, Huageng; Wang, Yujie; Ding, Mingyue

2013-10-01

Atherosclerotic lesions at the carotid artery are a major cause of emboli or atheromatous debris, resulting in approximately 88% of ischemic strokes in the USA in 2006. Stroke is becoming the most common cause of death worldwide, although patient management and prevention strategies have reduced stroke rate considerably over the past decades. Many research studies have been carried out on how to quantitatively evaluate local arterial effects for potential carotid disease treatments. As an inexpensive, convenient and fast means of detection, ultrasonic medical testing has been widespread in the world, so it is very practical to use ultrasound technology in the prevention and treatment of carotid atherosclerosis. This paper is dedicated to this field. Currently, many ultrasound image characteristics on carotid plaque have been proposed. After screening a large number of features (including 26 morphological and 85 texture features), we have got six shape characteristics and six texture characteristics in the combination. In order to test the validity and accuracy of these combined features, we have established a Back-Propagation (BP) neural network to classify atherosclerosis plaques between atorvastatin group and placebo group. The leave-one-case-out protocol was utilized on a database of 768 carotid ultrasound images of 12 patients (5 subjects of placebo group and 7 subjects of atorvastatin group) for the evaluation. The classification results showed that the combined features and classification have good recognition ability, with the overall accuracy 83.93%, sensitivity 82.14%, specificity 85.20%, positive predictive value 79.86%, negative predictive value 86.98%, Matthew's correlation coefficient 67.08%, and Youden's index 67.34%. And the receiver operating characteristic (ROC) curve in our test also performed well.

Multiple supervised residual network for osteosarcoma segmentation in CT images.

PubMed

Zhang, Rui; Huang, Lin; Xia, Wei; Zhang, Bo; Qiu, Bensheng; Gao, Xin

2018-01-01

Automatic and accurate segmentation of osteosarcoma region in CT images can help doctor make a reasonable treatment plan, thus improving cure rate. In this paper, a multiple supervised residual network (MSRN) was proposed for osteosarcoma image segmentation. Three supervised side output modules were added to the residual network. The shallow side output module could extract image shape features, such as edge features and texture features. The deep side output module could extract semantic features. The side output module could compute the loss value between output probability map and ground truth and back-propagate the loss information. Then, the parameters of residual network could be modified by gradient descent method. This could guide the multi-scale feature learning of the network. The final segmentation results were obtained by fusing the results output by the three side output modules. A total of 1900 CT images from 15 osteosarcoma patients were used to train the network and a total of 405 CT images from another 8 osteosarcoma patients were used to test the network. Results indicated that MSRN enabled a dice similarity coefficient (DSC) of 89.22%, a sensitivity of 88.74% and a F1-measure of 0.9305, which were larger than those obtained by fully convolutional network (FCN) and U-net. Thus, MSRN for osteosarcoma segmentation could give more accurate results than FCN and U-Net. Copyright © 2018 Elsevier Ltd. All rights reserved.

Fiber-channel audio video standard for military and commercial aircraft product lines

NASA Astrophysics Data System (ADS)

Keller, Jack E.

2002-08-01

Fibre channel is an emerging high-speed digital network technology that combines to make inroads into the avionics arena. The suitability of fibre channel for such applications is largely due to its flexibility in these key areas: Network topologies can be configured in point-to-point, arbitrated loop or switched fabric connections. The physical layer supports either copper or fiber optic implementations with a Bit Error Rate of less than 10-12. Multiple Classes of Service are available. Multiple Upper Level Protocols are supported. Multiple high speed data rates offer open ended growth paths providing speed negotiation within a single network. Current speeds supported by commercially available hardware are 1 and 2 Gbps providing effective data rates of 100 and 200 MBps respectively. Such networks lend themselves well to the transport of digital video and audio data. This paper summarizes an ANSI standard currently in the final approval cycle of the InterNational Committee for Information Technology Standardization (INCITS). This standard defines a flexible mechanism whereby digital video, audio and ancillary data are systematically packaged for transport over a fibre channel network. The basic mechanism, called a container, houses audio and video content functionally grouped as elements of the container called objects. Featured in this paper is a specific container mapping called Simple Parametric Digital Video (SPDV) developed particularly to address digital video in avionics systems. SPDV provides pixel-based video with associated ancillary data typically sourced by various sensors to be processed and/or distributed in the cockpit for presentation via high-resolution displays. Also highlighted in this paper is a streamlined Upper Level Protocol (ULP) called Frame Header Control Procedure (FHCP) targeted for avionics systems where the functionality of a more complex ULP is not required.

The study and implementation of the wireless network data security model

NASA Astrophysics Data System (ADS)

Lin, Haifeng

2013-03-01

In recent years, the rapid development of Internet technology and the advent of information age, people are increasing the strong demand for the information products and the market for information technology. Particularly, the network security requirements have become more sophisticated. This paper analyzes the wireless network in the data security vulnerabilities. And a list of wireless networks in the framework is the serious defects with the related problems. It has proposed the virtual private network technology and wireless network security defense structure; and it also given the wireless networks and related network intrusion detection model for the detection strategies.

Application of a neural network for reflectance spectrum classification

NASA Astrophysics Data System (ADS)

Yang, Gefei; Gartley, Michael

2017-05-01

Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

Research on intrusion detection based on Kohonen network and support vector machine

NASA Astrophysics Data System (ADS)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

A Review of Power Distribution Test Feeders in the United States and the Need for Synthetic Representative Networks

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

Postigo Marcos, Fernando E.; Domingo, Carlos Mateo; San Roman, Tomas Gomez

Under the increasing penetration of distributed energy resources and new smart network technologies, distribution utilities face new challenges and opportunities to ensure reliable operations, manage service quality, and reduce operational and investment costs. Simultaneously, the research community is developing algorithms for advanced controls and distribution automation that can help to address some of these challenges. However, there is a shortage of realistic test systems that are publically available for development, testing, and evaluation of such new algorithms. Concerns around revealing critical infrastructure details and customer privacy have severely limited the number of actual networks published and that are available formore » testing. In recent decades, several distribution test feeders and US-featured representative networks have been published, but the scale, complexity, and control data vary widely. This paper presents a first-of-a-kind structured literature review of published distribution test networks with a special emphasis on classifying their main characteristics and identifying the types of studies for which they have been used. As a result, this both aids researchers in choosing suitable test networks for their needs and highlights the opportunities and directions for further test system development. In particular, we highlight the need for building large-scale synthetic networks to overcome the identified drawbacks of current distribution test feeders.« less

A Review of Power Distribution Test Feeders in the United States and the Need for Synthetic Representative Networks

DOE PAGES

Postigo Marcos, Fernando E.; Domingo, Carlos Mateo; San Roman, Tomas Gomez; ...

2017-11-18

Under the increasing penetration of distributed energy resources and new smart network technologies, distribution utilities face new challenges and opportunities to ensure reliable operations, manage service quality, and reduce operational and investment costs. Simultaneously, the research community is developing algorithms for advanced controls and distribution automation that can help to address some of these challenges. However, there is a shortage of realistic test systems that are publically available for development, testing, and evaluation of such new algorithms. Concerns around revealing critical infrastructure details and customer privacy have severely limited the number of actual networks published and that are available formore » testing. In recent decades, several distribution test feeders and US-featured representative networks have been published, but the scale, complexity, and control data vary widely. This paper presents a first-of-a-kind structured literature review of published distribution test networks with a special emphasis on classifying their main characteristics and identifying the types of studies for which they have been used. As a result, this both aids researchers in choosing suitable test networks for their needs and highlights the opportunities and directions for further test system development. In particular, we highlight the need for building large-scale synthetic networks to overcome the identified drawbacks of current distribution test feeders.« less

Hybrid Network Defense Model Based on Fuzzy Evaluation

PubMed Central

2014-01-01

With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network's existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter's inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture. PMID:24574870

Enumeration of spanning trees in planar unclustered networks

NASA Astrophysics Data System (ADS)

Xiao, Yuzhi; Zhao, Haixing; Hu, Guona; Ma, Xiujuan

2014-07-01

Among a variety of subgraphs, spanning trees are one of the most important and fundamental categories. They are relevant to diverse aspects of networks, including reliability, transport, self-organized criticality, loop-erased random walks and so on. In this paper, we introduce a family of modular, self-similar planar networks with zero clustering. Relevant properties of this family are comparable to those networks associated with technological systems having low clustering, like power grids, some electronic circuits, the Internet and some biological systems. So, it is very significant to research on spanning trees of planar networks. However, for a large network, evaluating the relevant determinant is intractable. In this paper, we propose a fairly generic linear algorithm for counting the number of spanning trees of a planar network. Using the algorithm, we derive analytically the exact numbers of spanning trees in planar networks. Our result shows that the computational complexity is O(t) , which is better than that of the matrix tree theorem with O(m2t2) , where t is the number of steps and m is the girth of the planar network. We also obtain the entropy for the spanning trees of a given planar network. We find that the entropy of spanning trees in the studied network is small, which is in sharp contrast to the previous result for planar networks with the same average degree. We also determine an upper bound and a lower bound for the numbers of spanning trees in the family of planar networks by the algorithm. As another application of the algorithm, we give a formula for the number of spanning trees in an outerplanar network with small-world features.

Individual Morphological Brain Network Construction Based on Multivariate Euclidean Distances Between Brain Regions.

PubMed

Yu, Kaixin; Wang, Xuetong; Li, Qiongling; Zhang, Xiaohui; Li, Xinwei; Li, Shuyu

2018-01-01

Morphological brain network plays a key role in investigating abnormalities in neurological diseases such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, most of the morphological brain network construction methods only considered a single morphological feature. Each type of morphological feature has specific neurological and genetic underpinnings. A combination of morphological features has been proven to have better diagnostic performance compared with a single feature, which suggests that an individual morphological brain network based on multiple morphological features would be beneficial in disease diagnosis. Here, we proposed a novel method to construct individual morphological brain networks for two datasets by calculating the exponential function of multivariate Euclidean distance as the evaluation of similarity between two regions. The first dataset included 24 healthy subjects who were scanned twice within a 3-month period. The topological properties of these brain networks were analyzed and compared with previous studies that used different methods and modalities. Small world property was observed in all of the subjects, and the high reproducibility indicated the robustness of our method. The second dataset included 170 patients with MCI (86 stable MCI and 84 progressive MCI cases) and 169 normal controls (NC). The edge features extracted from the individual morphological brain networks were used to distinguish MCI from NC and separate MCI subgroups (progressive vs. stable) through the support vector machine in order to validate our method. The results showed that our method achieved an accuracy of 79.65% (MCI vs. NC) and 70.59% (stable MCI vs. progressive MCI) in a one-dimension situation. In a multiple-dimension situation, our method improved the classification performance with an accuracy of 80.53% (MCI vs. NC) and 77.06% (stable MCI vs. progressive MCI) compared with the method using a single feature. The results indicated that our method could effectively construct an individual morphological brain network based on multiple morphological features and could accurately discriminate MCI from NC and stable MCI from progressive MCI, and may provide a valuable tool for the investigation of individual morphological brain networks.

Hyperspectral Image Enhancement and Mixture Deep-Learning Classification of Corneal Epithelium Injuries.

PubMed

Noor, Siti Salwa Md; Michael, Kaleena; Marshall, Stephen; Ren, Jinchang

2017-11-16

In our preliminary study, the reflectance signatures obtained from hyperspectral imaging (HSI) of normal and abnormal corneal epithelium tissues of porcine show similar morphology with subtle differences. Here we present image enhancement algorithms that can be used to improve the interpretability of data into clinically relevant information to facilitate diagnostics. A total of 25 corneal epithelium images without the application of eye staining were used. Three image feature extraction approaches were applied for image classification: (i) image feature classification from histogram using a support vector machine with a Gaussian radial basis function (SVM-GRBF); (ii) physical image feature classification using deep-learning Convolutional Neural Networks (CNNs) only; and (iii) the combined classification of CNNs and SVM-Linear. The performance results indicate that our chosen image features from the histogram and length-scale parameter were able to classify with up to 100% accuracy; particularly, at CNNs and CNNs-SVM, by employing 80% of the data sample for training and 20% for testing. Thus, in the assessment of corneal epithelium injuries, HSI has high potential as a method that could surpass current technologies regarding speed, objectivity, and reliability.

Optical MEMS platform for low-cost on-chip integration of planar light circuits and optical switching

NASA Astrophysics Data System (ADS)

German, Kristine A.; Kubby, Joel; Chen, Jingkuang; Diehl, James; Feinberg, Kathleen; Gulvin, Peter; Herko, Larry; Jia, Nancy; Lin, Pinyen; Liu, Xueyuan; Ma, Jun; Meyers, John; Nystrom, Peter; Wang, Yao Rong

2004-07-01

Xerox Corporation has developed a technology platform for on-chip integration of latching MEMS optical waveguide switches and Planar Light Circuit (PLC) components using a Silicon On Insulator (SOI) based process. To illustrate the current state of this new technology platform, working prototypes of a Reconfigurable Optical Add/Drop Multiplexer (ROADM) and a l-router will be presented along with details of the integrated latching MEMS optical switches. On-chip integration of optical switches and PLCs can greatly reduce the size, manufacturing cost and operating cost of multi-component optical equipment. It is anticipated that low-cost, low-overhead optical network products will accelerate the migration of functions and services from high-cost long-haul markets to price sensitive markets, including networks for metropolitan areas and fiber to the home. Compared to the more common silica-on-silicon PLC technology, the high index of refraction of silicon waveguides created in the SOI device layer enables miniaturization of optical components, thereby increasing yield and decreasing cost projections. The latching SOI MEMS switches feature moving waveguides, and are advantaged across multiple attributes relative to alternative switching technologies, such as thermal optical switches and polymer switches. The SOI process employed was jointly developed under the auspice of the NIST APT program in partnership with Coventor, Corning IntelliSense Corp., and MicroScan Systems to enable fabrication of a broad range of free space and guided wave MicroOptoElectroMechanical Systems (MOEMS).

Improving deep convolutional neural networks with mixed maxout units

PubMed Central

Liu, Fu-xian; Li, Long-yue

2017-01-01

Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that “non-maximal features are unable to deliver” and “feature mapping subspace pooling is insufficient,” we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN) model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance. PMID:28727737

Machine learning classifier using abnormal brain network topological metrics in major depressive disorder.

PubMed

Guo, Hao; Cao, Xiaohua; Liu, Zhifen; Li, Haifang; Chen, Junjie; Zhang, Kerang

2012-12-05

Resting state functional brain networks have been widely studied in brain disease research. However, it is currently unclear whether abnormal resting state functional brain network metrics can be used with machine learning for the classification of brain diseases. Resting state functional brain networks were constructed for 28 healthy controls and 38 major depressive disorder patients by thresholding partial correlation matrices of 90 regions. Three nodal metrics were calculated using graph theory-based approaches. Nonparametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in six different algorithms. We used statistical significance as the threshold for selecting features and measured the accuracies of six classifiers with different number of features. A sensitivity analysis method was used to evaluate the importance of different features. The result indicated that some of the regions exhibited significantly abnormal nodal centralities, including the limbic system, basal ganglia, medial temporal, and prefrontal regions. Support vector machine with radial basis kernel function algorithm and neural network algorithm exhibited the highest average accuracy (79.27 and 78.22%, respectively) with 28 features (P<0.05). Correlation analysis between feature importance and the statistical significance of metrics was investigated, and the results revealed a strong positive correlation between them. Overall, the current study demonstrated that major depressive disorder is associated with abnormal functional brain network topological metrics and statistically significant nodal metrics can be successfully used for feature selection in classification algorithms.

Grain-size considerations for optoelectronic multistage interconnection networks.

PubMed

Krishnamoorthy, A V; Marchand, P J; Kiamilev, F E; Esener, S C

1992-09-10

This paper investigates, at the system level, the performance-cost trade-off between optical and electronic interconnects in an optoelectronic interconnection network. The specific system considered is a packet-switched, free-space optoelectronic shuffle-exchange multistage interconnection network (MIN). System bandwidth is used as the performance measure, while system area, system power, and system volume constitute the cost measures. A detailed design and analysis of a two-dimensional (2-D) optoelectronic shuffle-exchange routing network with variable grain size K is presented. The architecture permits the conventional 2 x 2 switches or grains to be generalized to larger K x K grain sizes by replacing optical interconnects with electronic wires without affecting the functionality of the system. Thus the system consists of log(k) N optoelectronic stages interconnected with free-space K-shuffles. When K = N, the MIN consists of a single electronic stage with optical input-output. The system design use an effi ient 2-D VLSI layout and a single diffractive optical element between stages to provide the 2-D K-shuffle interconnection. Results indicate that there is an optimum range of grain sizes that provides the best performance per cost. For the specific VLSI/GaAs multiple quantum well technology and system architecture considered, grain sizes larger than 256 x 256 result in a reduced performance, while grain sizes smaller than 16 x 16 have a high cost. For a network with 4096 channels, the useful range of grain sizes corresponds to approximately 250-400 electronic transistors per optical input-output channel. The effect of varying certain technology parameters such as the number of hologram phase levels, the modulator driving voltage, the minimum detectable power, and VLSI minimum feature size on the optimum grain-size system is studied. For instance, results show that using four phase levels for the interconnection hologram is a good compromise for the cost functions mentioned above. As VLSI minimum feature sizes decrease, the optimum grain size increases, whereas, if optical interconnect performance in terms of the detector power or modulator driving voltage requirements improves, the optimum grain size may be reduced. Finally, several architectural modifications to the system, such as K x K contention-free switches and sorting networks, are investigated and optimized for grain size. Results indicate that system bandwidth can be increased, but at the price of reduced performance/cost. The optoelectronic MIN architectures considered thus provide a broad range of performance/cost alternatives and offer a superior performance over purely electronic MIN's.

Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

PubMed

Ansari, Amir H; Cherian, Perumpillichira J; Caicedo, Alexander; Naulaers, Gunnar; De Vos, Maarten; Van Huffel, Sabine

2018-04-02

Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

Disrupted reward and cognitive control networks contribute to anhedonia in depression.

PubMed

Gong, Liang; He, Cancan; Zhang, Haisan; Zhang, Hongxing; Zhang, Zhijun; Xie, Chunming

2018-08-01

Neuroimaging studies have identified that anhedonia, a core feature of major depressive disorder (MDD), is associated with dysfunction in reward and cognitive control processing. However, it is still not clear how the reward network (β-network) and the cognitive control network (δ-network) are linked to biased anhedonia in MDD patients. Sixty-eight MDD patients and 64 cognitively normal (CN) subjects underwent a resting-state functional magnetic resonance imaging scan. A 2*2 ANCOVA analysis was used to explore the differences in the nucleus accumbens-based, voxelwise functional connectivity (FC) between the groups. Then, the β- and δ-networks were constructed, and the FC intensities were compared within and between theβ- and δ-networks across all subjects. Multiple linear regression analyses were also employed to investigate the relationships between the neural features of the β- and δ-networks and anhedonia in MDD patients. Compared to the CN subjects, the MDD patients showed synergistic functional decoupling in both the β- and δ-networks, as well as decreased FC intensities in the intra- and inter- β- and δ-networks. In addition, the FC in both the β- and δ-networks was significantly correlated with anhedonia severity in the MDD patients. Importantly, the integrated neural features of the β- and δ-networks could more precisely predict anhedonic symptoms. These findings initially demonstrated that the imbalance between β- and δ-network activity successfully predicted anhedonia severity and suggested that the neural features of both the β- and δ-networks could represent a fundamental mechanism that underlies anhedonia in MDD patients. Copyright © 2018 Elsevier Ltd. All rights reserved.

10 Management Controller for Time and Space Partitioning Architectures

NASA Astrophysics Data System (ADS)

Lachaize, Jerome; Deredempt, Marie-Helene; Galizzi, Julien

2015-09-01

The Integrated Modular Avionics (IMA) has been industrialized in aeronautical domain to enable the independent qualification of different application softwares from different suppliers on the same generic computer, this latter computer being a single terminal in a deterministic network. This concept allowed to distribute efficiently and transparently the different applications across the network, sizing accurately the HW equipments to embed on the aircraft, through the configuration of the virtual computers and the virtual network. , This concept has been studied for space domain and requirements issued [D04],[D05]. Experiments in the space domain have been done, for the computer level, through ESA and CNES initiatives [D02] [D03]. One possible IMA implementation may use Time and Space Partitioning (TSP) technology. Studies on Time and Space Partitioning [D02] for controlling resources access such as CPU and memories and studies on hardware/software interface standardization [D01] showed that for space domain technologies where I/O components (or IP) do not cover advanced features such as buffering, descriptors or virtualization, CPU overhead in terms of performances is mainly due to shared interface management in the execution platform, and to the high frequency of I/O accesses, these latter leading to an important number of context switches. This paper will present a solution to reduce this execution overhead with an open, modular and configurable controller.

Thermal feature extraction of servers in a datacenter using thermal image registration

NASA Astrophysics Data System (ADS)

Liu, Hang; Ran, Jian; Xie, Ting; Gao, Shan

2017-09-01

Thermal cameras provide fine-grained thermal information that enhances monitoring and enables automatic thermal management in large datacenters. Recent approaches employing mobile robots or thermal camera networks can already identify the physical locations of hot spots. Other distribution information used to optimize datacenter management can also be obtained automatically using pattern recognition technology. However, most of the features extracted from thermal images, such as shape and gradient, may be affected by changes in the position and direction of the thermal camera. This paper presents a method for extracting the thermal features of a hot spot or a server in a container datacenter. First, thermal and visual images are registered based on textural characteristics extracted from images acquired in datacenters. Then, the thermal distribution of each server is standardized. The features of a hot spot or server extracted from the standard distribution can reduce the impact of camera position and direction. The results of experiments show that image registration is efficient for aligning the corresponding visual and thermal images in the datacenter, and the standardization procedure reduces the impacts of camera position and direction on hot spot or server features.

Recent Advances of VCSEL Photonics

NASA Astrophysics Data System (ADS)

Koyama, Fumio

2006-12-01

A vertical-cavity surface emitting laser (VCSEL) was invented 30 years ago. A lot of unique features can be expected, such as low-power consumption, wafer-level testing, small packaging capability, and so on. The market of VCSELs has been growing up rapidly in recent years, and they are now key devices in local area networks using multimode optical fibers. Also, long wavelength VCSELs are currently attracting much interest for use in single-mode fiber metropolitan area and wide area network applications. In addition, a VCSEL-based disruptive technology enables various consumer applications such as a laser mouse and laser printers. In this paper, the recent advance of VCSEL photonics will be reviewed, which include the wavelength extension of single-mode VCSELs and their wavelength integration/control. Also, this paper explores the potential and challenges for new functions of VCSELs toward optical signal processing.

DebtRank a centrality measure for financial systems and beyond

NASA Astrophysics Data System (ADS)

Caldarelli, Guido; Battiston, Stefano; Puliga, Michelangelo; Kaushik, Rahul; Tasca, Paolo; Chair of System Design Collaboration; IMT Alti Studi Lucca Collaboration

2013-03-01

Use of network theory made possible to measure quantitatively many features of social and technological systems. In this spirit, inspired by traditional measures of centrality we introduce DebtRank a novel measure of systemic impact. We that we intend the risk of default of a large portion of the financial system, depends on the network of financial exposures among institutions. As an application, we analyse a new and unique dataset on the USD 1.2 trillion FED emergency loans program to global financial institutions during 2008-2010. We find that a group of 22 institutions, which received most of the funds, form a strongly connected graph where each of the nodes becomes systemically important at the peak of the crisis. Moreover, a systemic default could have been triggered even by small dispersed shocks. Other application to different systems are also presented.

Generalized hypercube structures and hyperswitch communication network

NASA Technical Reports Server (NTRS)

Young, Steven D.

1992-01-01

This paper discusses an ongoing study that uses a recent development in communication control technology to implement hybrid hypercube structures. These architectures are similar to binary hypercubes, but they also provide added connectivity between the processors. This added connectivity increases communication reliability while decreasing the latency of interprocessor message passing. Because these factors directly determine the speed that can be obtained by multiprocessor systems, these architectures are attractive for applications such as remote exploration and experimentation, where high performance and ultrareliability are required. This paper describes and enumerates these architectures and discusses how they can be implemented with a modified version of the hyperswitch communication network (HCN). The HCN is analyzed because it has three attractive features that enable these architectures to be effective: speed, fault tolerance, and the ability to pass multiple messages simultaneously through the same hyperswitch controller.

Radial-Velocity Signatures of Magnetic Features on the Sun Observed as a Star

NASA Astrophysics Data System (ADS)

Palumbo, M. L., III; Haywood, R. D.; Saar, S. H.; Dupree, A. K.; Milbourne, T. W.

2017-12-01

In recent years, the search for Earth-mass planets using radial-velocity measurements has become increasingly limited by signals arising from stellar activity. Individual magnetic features induce localized changes in intensity and velocity, which combine to change the apparent radial velocity of the star. Therefore it is critical to identify an indicator of activity-driven radial-velocity variations on the timescale of stellar rotation periods. We use 617.3 nm photospheric filtergrams, magnetograms, and dopplergrams from SDO/HMI and 170.0 nm chromospheric filtergrams from AIA to identify magnetically-driven solar features and reconstruct the integrated solar radial velocity with six samples per day over the course of 2014. Breaking the solar image up into regions of umbrae, penumbrae, quiet Sun, network, and plages, we find a distinct variation in the center-to-limb intensity-weighted velocity for each region. In agreement with past studies, we find that the suppression of convective blueshift is dominated by plages and network, rather than dark photospheric features. In the future, this work will be highly useful for identifying indicators which correlate with rotationally modulated radial-velocity variations. This will allow us to break the activity barrier that currently precludes the precise characterization of exoplanet properties at the lowest masses. This work was supported by the NSF-REU solar physics program at SAO, grant number AGS-1560313. This work was performed in part under contract with the California Institute of Technology (Caltech)/Jet Propulsion Laboratory (JPL) funded by NASA through the Sagan Fellowship Program executed by the NASA Exoplanet Science Institute.

A PDA study management tool (SMT) utilizing wireless broadband and full DICOM viewing capability

NASA Astrophysics Data System (ADS)

Documet, Jorge; Liu, Brent; Zhou, Zheng; Huang, H. K.; Documet, Luis

2007-03-01

During the last 4 years IPI (Image Processing and Informatics) Laboratory has been developing a web-based Study Management Tool (SMT) application that allows Radiologists, Film librarians and PACS-related (Picture Archiving and Communication System) users to dynamically and remotely perform Query/Retrieve operations in a PACS network. The users utilizing a regular PDA (Personal Digital Assistant) can remotely query a PACS archive to distribute any study to an existing DICOM (Digital Imaging and Communications in Medicine) node. This application which has proven to be convenient to manage the Study Workflow [1, 2] has been extended to include a DICOM viewing capability in the PDA. With this new feature, users can take a quick view of DICOM images providing them mobility and convenience at the same time. In addition, we are extending this application to Metropolitan-Area Wireless Broadband Networks. This feature requires Smart Phones that are capable of working as a PDA and have access to Broadband Wireless Services. With the extended application to wireless broadband technology and the preview of DICOM images, the Study Management Tool becomes an even more powerful tool for clinical workflow management.

A native IP satellite communications system

NASA Astrophysics Data System (ADS)

Koudelka, O.; Schmidt, M.; Ebert, J.; Schlemmer, H.; Kastner-Puschl, S.; Riedler, W.

2004-08-01

≪ In the framework of ESA's ARTES-5 program the Institute of Applied Systems Technology (Joanneum Research) in cooperation with the Department of Communications and Wave Propagation has developed a novel meshed satellite communications system which is optimised for Internet traffic and applications (L*IP—Local Network Interconnection via Satellite Systems Using the IP Protocol Suite). Both symmetrical and asymmetrical connections are supported. Bandwidth on demand and guaranteed quality of service are key features of the system. A novel multi-frequency TDMA access scheme utilises efficient methods of IP encapsulation. In contrast to other solutions it avoids legacy transport network techniques. While the DVB-RCS standard is based on ATM or MPEG transport cells, the solution of the L*IP system uses variable-length cells which reduces the overhead significantly. A flexible and programmable platform based on Linux machines was chosen to allow the easy implementation and adaptation to different standards. This offers the possibility to apply the system not only to satellite communications, but provides seamless integration with terrestrial fixed broadcast wireless access systems. The platform is also an ideal test-bed for a variety of interactive broadband communications systems. The paper describes the system architecture and the key features of the system.

Survey on paediatric tumour boards in Europe: current situation and results from the ExPo-r-Net project.

PubMed

Juan Ribelles, A; Berlanga, P; Schreier, G; Nitzlnader, M; Brunmair, B; Castel, V; Essiaf, S; Cañete, A; Ladenstein, R

2018-01-08

Under the ExPO-r-NeT project (European Expert Paediatric Oncology Reference Network for Diagnostics and Treatment), we aimed to identify paediatric oncology tumour boards in Europe to investigate the kind of technologies and logistics that are in place in different countries and to explore current differences between regions. A 20-question survey regarding several features of tumor boards was designed. Data collected included infrastructure, organization, and clinical decision-making information from the centres. The survey was distributed to the National Paediatric Haematology and Oncology Societies that forwarded the survey to the sites. For comparative analysis, respondents were grouped into four geographical regions. The questionnaire was distributed amongst 30 countries. Response was obtained from 23 (77%) that altogether have 212 paediatric oncology treating centres. A total of 121 institutions answered (57%). Ninety-one percent of the centres hold multidisciplinary boards; however, international second consultations are performed in 36% and only 15% participate on virtual tumor boards. Videoconferencing facilities and standard operational procedures (SOPs) are available in 49 and 43% of the centres, respectively. There were statistically significant differences between European regions concerning meeting infrastructure and organization/logistics: specific room, projecting equipment, access to medical records, videoconferencing facilities, and existence of SOPs. Paediatric tumor boards are a common feature in Europe. To reduce inequalities and have equal access to healthcare, a virtual network is needed. Important differences on the functioning and access to technology between regions in Europe have been observed and need to be addressed.

Design alternatives for wavelength routing networks

NASA Astrophysics Data System (ADS)

Miliotis, K.; Papadimitriou, G. I.; Pomportsis, A. S.

2003-03-01

This paper attempts to provide a high level overview of many of the technologies employed in optical networks with a focus on wavelength-routing networks. Optical networks involve a number of technologies from the physics of light through protocols and networks architectures. In fact there is so much technology and know-how that most people involved with optical networks only have a full understanding of the narrow area they deal with. We start first examining the principles that govern light and its use as a wave guide, and then turn our focus to the various components that constitute an optical network and conclude with the description of all optical networks and wavelength-routed networks in greater detail.

Non-invasive classification of gas-liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

NASA Astrophysics Data System (ADS)

Musa Abbagoni, Baba; Yeung, Hoi

2016-08-01

The identification of flow pattern is a key issue in multiphase flow which is encountered in the petrochemical industry. It is difficult to identify the gas-liquid flow regimes objectively with the gas-liquid two-phase flow. This paper presents the feasibility of a clamp-on instrument for an objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and an artificial neural network, which records and processes the ultrasonic signals reflected from the two-phase flow. Experimental data is obtained on a horizontal test rig with a total pipe length of 21 m and 5.08 cm internal diameter carrying air-water two-phase flow under slug, elongated bubble, stratified-wavy and, stratified flow regimes. Multilayer perceptron neural networks (MLPNNs) are used to develop the classification model. The classifier requires features as an input which is representative of the signals. Ultrasound signal features are extracted by applying both power spectral density (PSD) and discrete wavelet transform (DWT) methods to the flow signals. A classification scheme of ‘1-of-C coding method for classification’ was adopted to classify features extracted into one of four flow regime categories. To improve the performance of the flow regime classifier network, a second level neural network was incorporated by using the output of a first level networks feature as an input feature. The addition of the two network models provided a combined neural network model which has achieved a higher accuracy than single neural network models. Classification accuracies are evaluated in the form of both the PSD and DWT features. The success rates of the two models are: (1) using PSD features, the classifier missed 3 datasets out of 24 test datasets of the classification and scored 87.5% accuracy; (2) with the DWT features, the network misclassified only one data point and it was able to classify the flow patterns up to 95.8% accuracy. This approach has demonstrated the success of a clamp-on ultrasound sensor for flow regime classification that would be possible in industry practice. It is considerably more promising than other techniques as it uses a non-invasive and non-radioactive sensor.

Proceedings of the Workshop on Advanced Network and Technology Concepts for Mobile, Micro, and Personal Communications

NASA Technical Reports Server (NTRS)

Paul, Lori (Editor)

1991-01-01

The Workshop on Advanced Network and Technology Concepts for Mobile, Micro, and Personal Communications was held at NASA's JPL Laboratory on 30-31 May 1991. It provided a forum for reviewing the development of advanced network and technology concepts for turn-of-the-century telecommunications. The workshop was organized into three main categories: (1) Satellite-Based Networks (L-band, C-band, Ku-band, and Ka-band); (2) Terrestrial-Based Networks (cellular, CT2, PCN, GSM, and other networks); and (3) Hybrid Satellite/Terrestrial Networks. The proceedings contain presentation papers from each of the above categories.

75 FR 2433 - Special Conditions: Boeing Model 747-8/-8F Airplanes, Systems and Data Networks Security...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-15

... design features associated with the architecture and connectivity capabilities of the airplane's computer... novel or unusual design features: digital systems architecture composed of several connected networks. The architecture and network configuration may be used for, or interfaced with, a diverse set of...

Research on Network Defense Strategy Based on Honey Pot Technology

NASA Astrophysics Data System (ADS)

Hong, Jianchao; Hua, Ying

2018-03-01

As a new network security technology of active defense, The honeypot technology has become a very effective and practical method of decoy attackers. The thesis discusses the theory, structure, characteristic, design and implementation of Honeypot in detail. Aiming at the development of means of attack, put forward a kind of network defense technology based on honeypot technology, constructing a virtual Honeypot demonstrate the honeypot’s functions.

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

PubMed Central

López-de-Ipiña, Karmele; Alonso, Jesus-Bernardino; Travieso, Carlos Manuel; Solé-Casals, Jordi; Egiraun, Harkaitz; Faundez-Zanuy, Marcos; Ezeiza, Aitzol; Barroso, Nora; Ecay-Torres, Miriam; Martinez-Lage, Pablo; de Lizardui, Unai Martinez

2013-01-01

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients. PMID:23698268

Making sense of OMICS data in population-based environmental health studies.

PubMed

Kyrtopoulos, Soterios A

2013-08-01

Although experience from the application of OMICS technologies in population-based environmental health studies is still relatively limited, the accumulated evidence shows that it can allow the identification of features (genes, proteins, and metabolites), or sets of such features, which are targeted by particular exposures or correlate with disease risk. Such features or profiles can therefore serve as biomarkers of exposure or disease risk. Blood-based OMIC profiles appear to reflect to some extent events occurring in target tissues and are associated with toxicity or disease and therefore have the potential to facilitate the elucidation of exposure-disease relationships. Further progress in this direction requires better understanding of the significance of exposure-induced network perturbations for disease initiation and progression and the development of a framework that combines agnostic searches with the utilization of prior knowledge, taking account of particular elements which characterize the structure and evolution of complex systems and brings in principles of systems biology. Copyright © 2013 Wiley Periodicals, Inc.

Speech, Voice, and Communication.

PubMed

Johnson, Julia A

2017-01-01

Communication changes are an important feature of Parkinson's and include both motor and nonmotor features. This chapter will cover briefly the motor features affecting speech production and voice function before focusing on the nonmotor aspects. A description of the difficulties experienced by people with Parkinson's when trying to communicate effectively is presented along with some of the assessment tools and therapists' treatment options. The idea of clinical heterogeneity of PD and subtyping patients with different communication problems is explored and suggestions are made on how this may influence clinicians' treatment methods and choices so as to provide personalized therapy programmes. The importance of encouraging and supporting people to maintain social networks, employment, and leisure activities is stated as the key to achieving sustainability. Finally looking into the future, the emergence of new technologies is seen as providing further possibilities to support therapists in the goal of helping people with Parkinson's to maintain good communication skills throughout the course of the disease. © 2017 Elsevier Inc. All rights reserved.

Innovation network

PubMed Central

Acemoglu, Daron; Akcigit, Ufuk; Kerr, William R.

2016-01-01

Technological progress builds upon itself, with the expansion of invention in one domain propelling future work in linked fields. Our analysis uses 1.8 million US patents and their citation properties to map the innovation network and its strength. Past innovation network structures are calculated using citation patterns across technology classes during 1975–1994. The interaction of this preexisting network structure with patent growth in upstream technology fields has strong predictive power on future innovation after 1995. This pattern is consistent with the idea that when there is more past upstream innovation for a particular technology class to build on, then that technology class innovates more. PMID:27681628

Gene network inference by fusing data from diverse distributions

PubMed Central

Žitnik, Marinka; Zupan, Blaž

2015-01-01

Motivation: Markov networks are undirected graphical models that are widely used to infer relations between genes from experimental data. Their state-of-the-art inference procedures assume the data arise from a Gaussian distribution. High-throughput omics data, such as that from next generation sequencing, often violates this assumption. Furthermore, when collected data arise from multiple related but otherwise nonidentical distributions, their underlying networks are likely to have common features. New principled statistical approaches are needed that can deal with different data distributions and jointly consider collections of datasets. Results: We present FuseNet, a Markov network formulation that infers networks from a collection of nonidentically distributed datasets. Our approach is computationally efficient and general: given any number of distributions from an exponential family, FuseNet represents model parameters through shared latent factors that define neighborhoods of network nodes. In a simulation study, we demonstrate good predictive performance of FuseNet in comparison to several popular graphical models. We show its effectiveness in an application to breast cancer RNA-sequencing and somatic mutation data, a novel application of graphical models. Fusion of datasets offers substantial gains relative to inference of separate networks for each dataset. Our results demonstrate that network inference methods for non-Gaussian data can help in accurate modeling of the data generated by emergent high-throughput technologies. Availability and implementation: Source code is at https://github.com/marinkaz/fusenet. Contact: blaz.zupan@fri.uni-lj.si Supplementary information: Supplementary information is available at Bioinformatics online. PMID:26072487

Macromolecular networks and intelligence in microorganisms

PubMed Central

Westerhoff, Hans V.; Brooks, Aaron N.; Simeonidis, Evangelos; García-Contreras, Rodolfo; He, Fei; Boogerd, Fred C.; Jackson, Victoria J.; Goncharuk, Valeri; Kolodkin, Alexey

2014-01-01

Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of information and communication technology (ICT): they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity – particularly activity of the human brain – with a phenomenon we call “intelligence.” Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as “human” and “brain” out of the defining features of “intelligence,” all forms of life – from microbes to humans – exhibit some or all characteristics consistent with “intelligence.” We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo. PMID:25101076

Impact of dynamic rate coding aspects of mobile phone networks on forensic voice comparison.

PubMed

Alzqhoul, Esam A S; Nair, Balamurali B T; Guillemin, Bernard J

2015-09-01

Previous studies have shown that landline and mobile phone networks are different in their ways of handling the speech signal, and therefore in their impact on it. But the same is also true of the different networks within the mobile phone arena. There are two major mobile phone technologies currently in use today, namely the global system for mobile communications (GSM) and code division multiple access (CDMA) and these are fundamentally different in their design. For example, the quality of the coded speech in the GSM network is a function of channel quality, whereas in the CDMA network it is determined by channel capacity (i.e., the number of users sharing a cell site). This paper examines the impact on the speech signal of a key feature of these networks, namely dynamic rate coding, and its subsequent impact on the task of likelihood-ratio-based forensic voice comparison (FVC). Surprisingly, both FVC accuracy and precision are found to be better for both GSM- and CDMA-coded speech than for uncoded. Intuitively one expects FVC accuracy to increase with increasing coded speech quality. This trend is shown to occur for the CDMA network, but, surprisingly, not for the GSM network. Further, in respect to comparisons between these two networks, FVC accuracy for CDMA-coded speech is shown to be slightly better than for GSM-coded speech, particularly when the coded-speech quality is high, but in terms of FVC precision the two networks are shown to be very similar. Copyright © 2015 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.

General consumer communication tools for improved image management and communication in medicine.

PubMed

Rosset, Chantal; Rosset, Antoine; Ratib, Osman

2005-12-01

We elected to explore new technologies emerging on the general consumer market that can improve and facilitate image and data communication in medical and clinical environment. These new technologies developed for communication and storage of data can improve the user convenience and facilitate the communication and transport of images and related data beyond the usual limits and restrictions of a traditional picture archiving and communication systems (PACS) network. We specifically tested and implemented three new technologies provided on Apple computer platforms. (1) We adopted the iPod, a MP3 portable player with a hard disk storage, to easily and quickly move large number of DICOM images. (2) We adopted iChat, a videoconference and instant-messaging software, to transmit DICOM images in real time to a distant computer for conferencing teleradiology. (3) Finally, we developed a direct secure interface to use the iDisk service, a file-sharing service based on the WebDAV technology, to send and share DICOM files between distant computers. These three technologies were integrated in a new open-source image navigation and display software called OsiriX allowing for manipulation and communication of multimodality and multidimensional DICOM image data sets. This software is freely available as an open-source project at http://homepage.mac.com/rossetantoine/OsiriX. Our experience showed that the implementation of these technologies allowed us to significantly enhance the existing PACS with valuable new features without any additional investment or the need for complex extensions of our infrastructure. The added features such as teleradiology, secure and convenient image and data communication, and the use of external data storage services open the gate to a much broader extension of our imaging infrastructure to the outside world.

Big data; sensor networks and remotely-sensed data for mapping; feature extraction from lidar

NASA Astrophysics Data System (ADS)

Tlhabano, Lorato

2018-05-01

Unmanned aerial vehicles (UAVs) can be used for mapping in the close range domain, combining aerial and terrestrial photogrammetry and now the emergence of affordable platforms to carry these technologies has opened up new opportunities for mapping and modeling cadastral boundaries. At the current state mainly low cost UAVs fitted with sensors are used in mapping projects with low budgets, the amount of data produced by the UAVs can be enormous hence the need for big data techniques' and concepts. The past couple of years have witnessed the dramatic rise of low-cost UAVs fitted with high tech Lidar sensors and as such the UAVS have now reached a level of practical reliability and professionalism which allow the use of these systems as mapping platforms. UAV based mapping provides not only the required accuracy with respect to cadastral laws and policies as well as requirements for feature extraction from the data sets and maps produced, UAVs are also competitive to other measurement technologies in terms of economic aspects. In the following an overview on how the various technologies of UAVs, big data concepts and lidar sensor technologies can work together to revolutionize cadastral mapping particularly in Africa and as a test case Botswana in particular will be used to investigate these technologies. These technologies can be combined to efficiently provide cadastral mapping in difficult to reach areas and over large areas of land similar to the Land Administration Procedures, Capacity and Systems (LAPCAS) exercise which was recently undertaken by the Botswana government, we will show how the uses of UAVS fitted with lidar sensor and utilizing big data concepts could have reduced not only costs and time for our government but also how UAVS could have provided more detailed cadastral maps.

Dissemination of Technology to Evaluate Healthy Food Incentive Programs.

PubMed

Freedman, Darcy A; Hunt, Alan R; Merritt, Katie; Shon, En-Jung; Pike, Stephanie N

2017-03-01

Federal policy supports increased implementation of monetary incentive interventions for chronic disease prevention among low-income populations. This study describes how a Prevention Research Center, working with a dissemination partner, developed and distributed technology to support nationwide implementation and evaluation of healthy food incentive programming focused on Supplemental Nutrition Assistance Program recipients. FM Tracks, an iOS-based application and website, was developed to standardize evaluation methods for healthy food incentive program implementation at direct-to-consumer markets. This evaluation examined diffusion and adoption of the technology over 9 months (July 2015-March 2016). Data were analyzed in 2016. FM Tracks was disseminated to 273 markets affiliated with 37 regional networks in 18 states and Washington, DC. All markets adopted the sales transaction data collection feature, with nearly all recording at least one Supplemental Nutrition Assistance Program (99.3%) and healthy food incentive (97.1%) transaction. A total of 43,493 sales transactions were recorded. By the ninth month of technology dissemination, markets were entering individual sales transactions using the application (34.5%) and website (29.9%) and aggregated transactions via website (35.6%) at similar rates. Use of optional evaluation features like recording a customer ID with individual transactions increased successively with a low of 22.2% during the first month to a high of 69.2% in the ninth month. Systematic and widely used evaluation technology creates possibilities for pragmatic research embedded within ongoing, real-world implementation of food access interventions. Technology dissemination requires supportive technical assistance and continuous refinement that can be advanced through academic-practitioner partnerships. Copyright © 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

PubMed

Lee, Christine K; Hofer, Ira; Gabel, Eilon; Baldi, Pierre; Cannesson, Maxime

2018-04-17

The authors tested the hypothesis that deep neural networks trained on intraoperative features can predict postoperative in-hospital mortality. The data used to train and validate the algorithm consists of 59,985 patients with 87 features extracted at the end of surgery. Feed-forward networks with a logistic output were trained using stochastic gradient descent with momentum. The deep neural networks were trained on 80% of the data, with 20% reserved for testing. The authors assessed improvement of the deep neural network by adding American Society of Anesthesiologists (ASA) Physical Status Classification and robustness of the deep neural network to a reduced feature set. The networks were then compared to ASA Physical Status, logistic regression, and other published clinical scores including the Surgical Apgar, Preoperative Score to Predict Postoperative Mortality, Risk Quantification Index, and the Risk Stratification Index. In-hospital mortality in the training and test sets were 0.81% and 0.73%. The deep neural network with a reduced feature set and ASA Physical Status classification had the highest area under the receiver operating characteristics curve, 0.91 (95% CI, 0.88 to 0.93). The highest logistic regression area under the curve was found with a reduced feature set and ASA Physical Status (0.90, 95% CI, 0.87 to 0.93). The Risk Stratification Index had the highest area under the receiver operating characteristics curve, at 0.97 (95% CI, 0.94 to 0.99). Deep neural networks can predict in-hospital mortality based on automatically extractable intraoperative data, but are not (yet) superior to existing methods.

From fuzzy recurrence plots to scalable recurrence networks of time series

NASA Astrophysics Data System (ADS)

Pham, Tuan D.

2017-04-01

Recurrence networks, which are derived from recurrence plots of nonlinear time series, enable the extraction of hidden features of complex dynamical systems. Because fuzzy recurrence plots are represented as grayscale images, this paper presents a variety of texture features that can be extracted from fuzzy recurrence plots. Based on the notion of fuzzy recurrence plots, defuzzified, undirected, and unweighted recurrence networks are introduced. Network measures can be computed for defuzzified recurrence networks that are scalable to meet the demand for the network-based analysis of big data.

Segmentation of retinal blood vessels using artificial neural networks for early detection of diabetic retinopathy

NASA Astrophysics Data System (ADS)

Mann, Kulwinder S.; Kaur, Sukhpreet

2017-06-01

There are various eye diseases in the patients suffering from the diabetes which includes Diabetic Retinopathy, Glaucoma, Hypertension etc. These all are the most common sight threatening eye diseases due to the changes in the blood vessel structure. The proposed method using supervised methods concluded that the segmentation of the retinal blood vessels can be performed accurately using neural networks training. It uses features which include Gray level features; Moment Invariant based features, Gabor filtering, Intensity feature, Vesselness feature for feature vector computation. Then the feature vector is calculated using only the prominent features.

Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks

NASA Astrophysics Data System (ADS)

Ma, Xiaoke; Sun, Penggang; Wang, Yu

2018-04-01

Many networks derived from society and nature are temporal and incomplete. The temporal link prediction problem in networks is to predict links at time T + 1 based on a given temporal network from time 1 to T, which is essential to important applications. The current algorithms either predict the temporal links by collapsing the dynamic networks or collapsing features derived from each network, which are criticized for ignoring the connection among slices. to overcome the issue, we propose a novel graph regularized nonnegative matrix factorization algorithm (GrNMF) for the temporal link prediction problem without collapsing the dynamic networks. To obtain the feature for each network from 1 to t, GrNMF factorizes the matrix associated with networks by setting the rest networks as regularization, which provides a better way to characterize the topological information of temporal links. Then, the GrNMF algorithm collapses the feature matrices to predict temporal links. Compared with state-of-the-art methods, the proposed algorithm exhibits significantly improved accuracy by avoiding the collapse of temporal networks. Experimental results of a number of artificial and real temporal networks illustrate that the proposed method is not only more accurate but also more robust than state-of-the-art approaches.

A link prediction method for heterogeneous networks based on BP neural network

NASA Astrophysics Data System (ADS)

Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu

2018-04-01

Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

Variogram-based feature extraction for neural network recognition of logos

NASA Astrophysics Data System (ADS)

Pham, Tuan D.

2003-03-01

This paper presents a new approach for extracting spatial features of images based on the theory of regionalized variables. These features can be effectively used for automatic recognition of logo images using neural networks. Experimental results on a public-domain logo database show the effectiveness of the proposed approach.

deepNF: Deep network fusion for protein function prediction.

PubMed

Gligorijevic, Vladimir; Barot, Meet; Bonneau, Richard

2018-06-01

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

The Use of Technology in Participant Tracking and Study Retention: Lessons Learned From a Clinical Trials Network Study.

PubMed

Mitchell, Shannon Gwin; Schwartz, Robert P; Alvanzo, Anika A H; Weisman, Monique S; Kyle, Tiffany L; Turrigiano, Eva M; Gibson, Martha L; Perez, Livangelie; McClure, Erin A; Clingerman, Sara; Froias, Autumn; Shandera, Danielle R; Walker, Robrina; Babcock, Dean L; Bailey, Genie L; Miele, Gloria M; Kunkel, Lynn E; Norton, Michael; Stitzer, Maxine L

2015-01-01

The growing use of newer communication and Internet technologies, even among low-income and transient populations, require research staff to update their outreach strategies to ensure high follow-up and participant retention rates. This paper presents the views of research assistants on the use of cell phones and the Internet to track participants in a multisite randomized trial of substance use disorder treatment. Preinterview questionnaires exploring tracking and other study-related activities were collected from 21 research staff across the 10 participating US sites. Data were then used to construct a semistructured interview guide that, in turn, was used to interview 12 of the same staff members. The questionnaires and interview data were entered in Atlas.ti and analyzed for emergent themes related to the use of technology for participant-tracking purposes. Study staff reported that most participants had cell phones, despite having unstable physical addresses and landlines. The incoming call feature of most cell phones was useful for participants and research staff alike, and texting proved to have additional benefits. However, reliance on participants' cell phones also proved problematic. Even homeless participants were found to have access to the Internet through public libraries and could respond to study staff e-mails. Some study sites opened generic social media accounts, through which study staff sent private messages to participants. However, the institutional review board (IRB) approval process for tracking participants using social media at some sites was prohibitively lengthy. Internet searches through Google, national paid databases, obituaries, and judiciary Web sites were also helpful tools. Research staff perceive that cell phones, Internet searches, and social networking sites were effective tools to achieve high follow-up rates in drug abuse research. Studies should incorporate cell phone, texting, and social network Web site information on locator forms; obtain IRB approval for contacting participants using social networking Web sites; and include Web searches, texting, and the use of social media in staff training as standard operating procedures.

Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT.

PubMed

Lopez-Martin, Manuel; Carro, Belen; Sanchez-Esguevillas, Antonio; Lloret, Jaime

2017-08-26

The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host's network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery.

Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT

PubMed Central

Carro, Belen; Sanchez-Esguevillas, Antonio

2017-01-01

The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host’s network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery. PMID:28846608

NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms

PubMed Central

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available. PMID:24667482

Biased ART: a neural architecture that shifts attention toward previously disregarded features following an incorrect prediction.

PubMed

Carpenter, Gail A; Gaddam, Sai Chaitanya

2010-04-01

Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved successful for both brain models and applications, computational examples show that attention to early critical features may later distort memory representations during online fast learning. For supervised learning, biased ARTMAP (bARTMAP) solves the problem of over-emphasis on early critical features by directing attention away from previously attended features after the system makes a predictive error. Small-scale, hand-computed analog and binary examples illustrate key model dynamics. Two-dimensional simulation examples demonstrate the evolution of bARTMAP memories as they are learned online. Benchmark simulations show that featural biasing also improves performance on large-scale examples. One example, which predicts movie genres and is based, in part, on the Netflix Prize database, was developed for this project. Both first principles and consistent performance improvements on all simulation studies suggest that featural biasing should be incorporated by default in all ARTMAP systems. Benchmark datasets and bARTMAP code are available from the CNS Technology Lab Website: http://techlab.bu.edu/bART/. Copyright 2009 Elsevier Ltd. All rights reserved.

Mobile Computing and Ubiquitous Networking: Concepts, Technologies and Challenges.

ERIC Educational Resources Information Center

Pierre, Samuel

2001-01-01

Analyzes concepts, technologies and challenges related to mobile computing and networking. Defines basic concepts of cellular systems. Describes the evolution of wireless technologies that constitute the foundations of mobile computing and ubiquitous networking. Presents characterization and issues of mobile computing. Analyzes economical and…

US long distance fiber optic networks: Technology, evolution and advanced concepts. Volume 2: Fiber optic technology and long distance networks

NASA Astrophysics Data System (ADS)

1986-10-01

The study projects until 2000 the evolution of long distance fiber optic networks in the U.S. Volume 1 is the Executive Summary. Volume 2 focuses on fiber optic components and systems that are directly related to the operation of long-haul networks. Optimistic, pessimistic and most likely scenarios of technology development are presented. The activities of national and regional companies implementing fiber long haul networks are also highlighted, along with an analysis of the market and regulatory forces affecting network evolution. Volume 3 presents advanced fiber optic network concept definitions. Inter-LATA traffic is quantified and forms the basis for the construction of 11-, 15-, 17-, and 23-node networks. Using the technology projections from Volume 2, a financial model identifies cost drivers and determines circuit mile costs between any two LATAs. A comparison of fiber optics with alternative transmission concludes the report.

US long distance fiber optic networks: Technology, evolution and advanced concepts. Volume 2: Fiber optic technology and long distance networks

NASA Technical Reports Server (NTRS)

1986-01-01

The study projects until 2000 the evolution of long distance fiber optic networks in the U.S. Volume 1 is the Executive Summary. Volume 2 focuses on fiber optic components and systems that are directly related to the operation of long-haul networks. Optimistic, pessimistic and most likely scenarios of technology development are presented. The activities of national and regional companies implementing fiber long haul networks are also highlighted, along with an analysis of the market and regulatory forces affecting network evolution. Volume 3 presents advanced fiber optic network concept definitions. Inter-LATA traffic is quantified and forms the basis for the construction of 11-, 15-, 17-, and 23-node networks. Using the technology projections from Volume 2, a financial model identifies cost drivers and determines circuit mile costs between any two LATAs. A comparison of fiber optics with alternative transmission concludes the report.

Propagation, cascades, and agreement dynamics in complex communication and social networks

NASA Astrophysics Data System (ADS)

Lu, Qiming

Many modern and important technological, social, information and infrastructure systems can be viewed as complex systems with a large number of interacting components. Models of complex networks and dynamical interactions, as well as their applications are of fundamental interests in many aspects. Here, several stylized models of multiplex propagation and opinion dynamics are investigated on complex and empirical social networks. We first investigate cascade dynamics in threshold-controlled (multiplex) propagation on random geometric networks. We find that such local dynamics can serve as an efficient, robust, and reliable prototypical activation protocol in sensor networks in responding to various alarm scenarios. We also consider the same dynamics on a modified network by adding a few long-range communication links, resulting in a small-world network. We find that such construction can further enhance and optimize the speed of the network's response, while keeping energy consumption at a manageable level. We also investigate a prototypical agent-based model, the Naming Game, on two-dimensional random geometric networks. The Naming Game [A. Baronchelli et al., J. Stat. Mech.: Theory Exp. (2006) P06014.] is a minimal model, employing local communications that captures the emergence of shared communication schemes (languages) in a population of autonomous semiotic agents. Implementing the Naming Games with local broadcasts on random geometric graphs, serves as a model for agreement dynamics in large-scale, autonomously operating wireless sensor networks. Further, it captures essential features of the scaling properties of the agreement process for spatially-embedded autonomous agents. Among the relevant observables capturing the temporal properties of the agreement process, we investigate the cluster-size distribution and the distribution of the agreement times, both exhibiting dynamic scaling. We also present results for the case when a small density of long-range communication links are added on top of the random geometric graph, resulting in a "small-world"-like network and yielding a significantly reduced time to reach global agreement. We construct a finite-size scaling analysis for the agreement times in this case. When applying the model of Naming Game on empirical social networks, this stylized agent-based model captures essential features of agreement dynamics in a network of autonomous agents, corresponding to the development of shared classification schemes in a network of artificial agents or opinion spreading and social dynamics in social networks. Our study focuses on the impact that communities in the underlying social graphs have on the outcome of the agreement process. We find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely. Further, we investigate agent-based network strategies to facilitate convergence to global consensus.

Features and heterogeneities in growing network models

NASA Astrophysics Data System (ADS)

Ferretti, Luca; Cortelezzi, Michele; Yang, Bin; Marmorini, Giacomo; Bianconi, Ginestra

2012-06-01

Many complex networks from the World Wide Web to biological networks grow taking into account the heterogeneous features of the nodes. The feature of a node might be a discrete quantity such as a classification of a URL document such as personal page, thematic website, news, blog, search engine, social network, etc., or the classification of a gene in a functional module. Moreover the feature of a node can be a continuous variable such as the position of a node in the embedding space. In order to account for these properties, in this paper we provide a generalization of growing network models with preferential attachment that includes the effect of heterogeneous features of the nodes. The main effect of heterogeneity is the emergence of an “effective fitness” for each class of nodes, determining the rate at which nodes acquire new links. The degree distribution exhibits a multiscaling behavior analogous to the the fitness model. This property is robust with respect to variations in the model, as long as links are assigned through effective preferential attachment. Beyond the degree distribution, in this paper we give a full characterization of the other relevant properties of the model. We evaluate the clustering coefficient and show that it disappears for large network size, a property shared with the Barabási-Albert model. Negative degree correlations are also present in this class of models, along with nontrivial mixing patterns among features. We therefore conclude that both small clustering coefficients and disassortative mixing are outcomes of the preferential attachment mechanism in general growing networks.

Learning representations for the early detection of sepsis with deep neural networks.

PubMed

Kam, Hye Jin; Kim, Ha Young

2017-10-01

Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. Study group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. With deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. The contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Ivanov, Ilia N.; Simpson, John T.

A method of preparing a network comprises disposing a solution comprising particulate materials in a solvent onto a superhydrophobic surface comprising a plurality of superhydrophobic features and interfacial areas between the superhydrophobic features. The plurality of superhydrophobic features has a water contact angle of at least about 150.degree.. The method of preparing the network also comprises removing the solvent from the solution of the particulate materials, and forming a network of the particulate materials in the interfacial areas, the particulate materials receding to the interfacial areas as the solvent is removed.

Retina Image Screening and Analysis Software Version 2.0

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

Tobin, Jr., Kenneth W.; Karnowski, Thomas P.; Aykac, Deniz

2009-04-01

The software allows physicians or researchers to ground-truth images of retinas, identifying key physiological features and lesions that are indicative of disease. The software features methods to automatically detect the physiological features and lesions. The software contains code to measure the quality of images received from a telemedicine network; create and populate a database for a telemedicine network; review and report the diagnosis of a set of images; and also contains components to transmit images from a Zeiss camera to the network through SFTP.

Ethernet-Based Services for Next Generation Networks

NASA Astrophysics Data System (ADS)

Hernandez-Valencia, Enrique

Over the last few years, Ethernet technology and services have emerged as an indispensable component of the broadband networking and telecommunications infrastructure, both for network operators and service providers. As an example, Worldwide Enterprise customer demand for Ethernet services by itself is expected to hit the 30B US mark by year 2012. Use of Ethernet technology in the feeder networks that support residential applications, such as "triple play" voice, data, and video services, is equally on the rise. As the synergies between packet-aware transport and service oriented equipment continue to be exploited in the path toward transport convergence. Ethernet technology is expected to play a critical part in the evolution toward converged Optical/Packet Transport networks. Here we discuss the main business motivations, services, and technologies driving the specifications of so-called carrier Ethernet and highlight challenges associated with delivering the expectations for low implementation complexity, easy of use, provisioning and management of networks and network elements embracing this technology.

Organization of excitable dynamics in hierarchical biological networks.

PubMed

Müller-Linow, Mark; Hilgetag, Claus C; Hütt, Marc-Thorsten

2008-09-26

This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.

Capacity Building for Research and Education in GIS/GPS Technology and Systems

DTIC Science & Technology

2015-05-20

In multi- sensor area Wireless Sensor Networking (WSN) fields will be explored. As a step forward the research to be conducted in WSN field is to...Agriculture Using Technology for Crops Scouting in Agriculture Application of Technology in Precision Agriculture Wireless Sensor Network (WSN) in...Cooperative Engagement Capability Range based algorithms for Wireless Sensor Network Self-configurable Wireless Sensor Network Energy Efficient Wireless

Discussion on the Technology and Method of Computer Network Security Management

NASA Astrophysics Data System (ADS)

Zhou, Jianlei

2017-09-01

With the rapid development of information technology, the application of computer network technology has penetrated all aspects of society, changed people's way of life work to a certain extent, brought great convenience to people. But computer network technology is not a panacea, it can promote the function of social development, but also can cause damage to the community and the country. Due to computer network’ openness, easiness of sharing and other characteristics, it had a very negative impact on the computer network security, especially the loopholes in the technical aspects can cause damage on the network information. Based on this, this paper will do a brief analysis on the computer network security management problems and security measures.

Driver-centred vehicle automation: using network analysis for agent-based modelling of the driver in highly automated driving systems.

PubMed

Banks, Victoria A; Stanton, Neville A

2016-11-01

To the average driver, the concept of automation in driving infers that they can become completely 'hands and feet free'. This is a common misconception, however, one that has been shown through the application of Network Analysis to new Cruise Assist technologies that may feature on our roads by 2020. Through the adoption of a Systems Theoretic approach, this paper introduces the concept of driver-initiated automation which reflects the role of the driver in highly automated driving systems. Using a combination of traditional task analysis and the application of quantitative network metrics, this agent-based modelling paper shows how the role of the driver remains an integral part of the driving system implicating the need for designers to ensure they are provided with the tools necessary to remain actively in-the-loop despite giving increasing opportunities to delegate their control to the automated subsystems. Practitioner Summary: This paper describes and analyses a driver-initiated command and control system of automation using representations afforded by task and social networks to understand how drivers remain actively involved in the task. A network analysis of different driver commands suggests that such a strategy does maintain the driver in the control loop.

Integrated multimedia information system on interactive CATV network

NASA Astrophysics Data System (ADS)

Lee, Meng-Huang; Chang, Shin-Hung

1998-10-01

In the current CATV system architectures, they provide one- way delivery of a common menu of entertainment to all the homes through the cable network. Through the technologies evolution, the interactive services (or two-way services) can be provided in the cable TV systems. They can supply customers with individualized programming and support real- time two-way communications. With a view to the service type changed from the one-way delivery systems to the two-way interactive systems, `on demand services' is a distinct feature of multimedia systems. In this paper, we present our work of building up an integrated multimedia system on interactive CATV network in Shih Chien University. Besides providing the traditional analog TV programming from the cable operator, we filter some channels to reserve them as our campus information channels. In addition to the analog broadcasting channel, the system also provides the interactive digital multimedia services, e.g. Video-On- Demand (VOD), Virtual Reality, BBS, World-Wide-Web, and Internet Radio Station. These two kinds of services are integrated in a CATV network by the separation of frequency allocation for the analog broadcasting service and the digital interactive services. Our ongoing work is to port our previous work of building up a VOD system conformed to DAVIC standard (for inter-operability concern) on Ethernet network into the current system.

Dynamics of feature categorization.

PubMed

Martí, Daniel; Rinzel, John

2013-01-01

In visual and auditory scenes, we are able to identify shared features among sensory objects and group them according to their similarity. This grouping is preattentive and fast and is thought of as an elementary form of categorization by which objects sharing similar features are clustered in some abstract perceptual space. It is unclear what neuronal mechanisms underlie this fast categorization. Here we propose a neuromechanistic model of fast feature categorization based on the framework of continuous attractor networks. The mechanism for category formation does not rely on learning and is based on biologically plausible assumptions, for example, the existence of populations of neurons tuned to feature values, feature-specific interactions, and subthreshold-evoked responses upon the presentation of single objects. When the network is presented with a sequence of stimuli characterized by some feature, the network sums the evoked responses and provides a running estimate of the distribution of features in the input stream. If the distribution of features is structured into different components or peaks (i.e., is multimodal), recurrent excitation amplifies the response of activated neurons, and categories are singled out as emerging localized patterns of elevated neuronal activity (bumps), centered at the centroid of each cluster. The emergence of bump states through sequential, subthreshold activation and the dependence on input statistics is a novel application of attractor networks. We show that the extraction and representation of multiple categories are facilitated by the rich attractor structure of the network, which can sustain multiple stable activity patterns for a robust range of connectivity parameters compatible with cortical physiology.

Neural net target-tracking system using structured laser patterns

NASA Astrophysics Data System (ADS)

Cho, Jae-Wan; Lee, Yong-Bum; Lee, Nam-Ho; Park, Soon-Yong; Lee, Jongmin; Choi, Gapchu; Baek, Sunghyun; Park, Dong-Sun

1996-06-01

In this paper, we describe a robot endeffector tracking system using sensory information from recently-announced structured pattern laser diodes, which can generate images with several different types of structured pattern. The neural network approach is employed to recognize the robot endeffector covering the situation of three types of motion: translation, scaling and rotation. Features for the neural network to detect the position of the endeffector are extracted from the preprocessed images. Artificial neural networks are used to store models and to match with unknown input features recognizing the position of the robot endeffector. Since a minimal number of samples are used for different directions of the robot endeffector in the system, an artificial neural network with the generalization capability can be utilized for unknown input features. A feedforward neural network with the generalization capability can be utilized for unknown input features. A feedforward neural network trained with the back propagation learning is used to detect the position of the robot endeffector. Another feedforward neural network module is used to estimate the motion from a sequence of images and to control movements of the robot endeffector. COmbining the tow neural networks for recognizing the robot endeffector and estimating the motion with the preprocessing stage, the whole system keeps tracking of the robot endeffector effectively.

Neural network modeling of a dolphin's sonar discrimination capabilities.

PubMed

Au, W W; Andersen, L N; Rasmussen, A R; Roitblat, H L; Nachtigall, P E

1995-07-01

The capability of an echolocating dolphin to discriminate differences in the wall thickness of cylinders was previously modeled by a counterpropagation neural network using only spectral information from the echoes. In this study, both time and frequency information were used to model the dolphin discrimination capabilities. Echoes from the same cylinders were digitized using a broadband simulated dolphin sonar signal with the transducer mounted on the dolphin's pen. The echoes were filtered by a bank of continuous constant-Q digital filters and the energy from each filter was computed in time increments of 1/bandwidth. Echo features of the standard and each comparison target were analyzed in pairs by a counterpropagation neural network, a backpropagation neural network, and a model using Euclidean distance measures. The backpropagation network performed better than both the counterpropagation network, and the Euclidean model, using either spectral-only features or combined temporal and spectral features. All models performed better using features containing both temporal and spectral information. The backpropagation network was able to perform better than the dolphins for noise-free echoes with Q values as low as 2 and 3. For a Q of 2, only temporal information was available. However, with noisy data, the network required a Q of 8 in order to perform as well as the dolphin.

Deployment of a Testbed in a Brazilian Research Network using IPv6 and Optical Access Technologies

NASA Astrophysics Data System (ADS)

Martins, Luciano; Ferramola Pozzuto, João; Olimpio Tognolli, João; Chaves, Niudomar Siqueira De A.; Reggiani, Atilio Eduardo; Hortêncio, Claudio Antonio

2012-04-01

This article presents the implementation of a testbed and the experimental results obtained with it on the Brazilian Experimental Network of the government-sponsored "GIGA Project." The use of IPv6 integrated to current and emerging optical architectures and technologies, such as dense wavelength division multiplexing and 10-gigabit Ethernet on the core and gigabit capable passive optical network and optical distribution network on access, were tested. These protocols, architectures, and optical technologies are promising and part of a brand new worldwide technological scenario that has being fairly adopted in the networks of enterprises and providers of the world.

Feature pruning by upstream drainage area to support automated generalization of the United States National Hydrography Dataset

USGS Publications Warehouse

Stanislawski, L.V.

2009-01-01

The United States Geological Survey has been researching generalization approaches to enable multiple-scale display and delivery of geographic data. This paper presents automated methods to prune network and polygon features of the United States high-resolution National Hydrography Dataset (NHD) to lower resolutions. Feature-pruning rules, data enrichment, and partitioning are derived from knowledge of surface water, the NHD model, and associated feature specification standards. Relative prominence of network features is estimated from upstream drainage area (UDA). Network and polygon features are pruned by UDA and NHD reach code to achieve a drainage density appropriate for any less detailed map scale. Data partitioning maintains local drainage density variations that characterize the terrain. For demonstration, a 48 subbasin area of 1:24 000-scale NHD was pruned to 1:100 000-scale (100 K) and compared to a benchmark, the 100 K NHD. The coefficient of line correspondence (CLC) is used to evaluate how well pruned network features match the benchmark network. CLC values of 0.82 and 0.77 result from pruning with and without partitioning, respectively. The number of polygons that remain after pruning is about seven times that of the benchmark, but the area covered by the polygons that remain after pruning is only about 10% greater than the area covered by benchmark polygons. ?? 2009.

A Study for the Feature Selection to Identify GIEMSA-Stained Human Chromosomes Based on Artificial Neural Network

DTIC Science & Technology

2001-10-25

neural network (ANN) has been adopted for the human chromosome classification. It is important to select optimum features for training neural network...Many studies for computer-based chromosome analysis have shown that it is possible to classify chromosomes into 24 subgroups. In addition, artificial

Technologies for unattended network operations

NASA Technical Reports Server (NTRS)

Jaworski, Allan; Odubiyi, Jide; Holdridge, Mark; Zuzek, John

1991-01-01

The necessary network management functions for a telecommunications, navigation and information management (TNIM) system in the framework of an extension of the ISO model for communications network management are described. Various technologies that could substantially reduce the need for TNIM network management, automate manpower intensive functions, and deal with synchronization and control at interplanetary distances are presented. Specific technologies addressed include the use of the ISO Common Management Interface Protocol, distributed artificial intelligence for network synchronization and fault management, and fault-tolerant systems engineering.

ICT approaches to integrating institutional and non-institutional data services for better understanding of hydro-meteorological phenomena

NASA Astrophysics Data System (ADS)

Bedrina, T.; Parodi, A.; Quarati, A.; Clematis, A.

2012-06-01

It is widely recognised that an effective exploitation of Information and Communication Technologies (ICT) is an enabling factor to achieve major advancements in Hydro-Meteorological Research (HMR). Recently, a lot of attention has been devoted to the use of ICT in HMR activities, e.g. in order to facilitate data exchange and integration, to improve computational capabilities and consequently model resolution and quality. Nowadays, ICT technologies have demonstrated that it is possible to extend monitoring networks by integrating sensors and other sources of data managed by volunteer's communities. These networks are constituted by peers that span a wide portion of the territory in many countries. The peers are "location aware" in the sense that they provide information strictly related with their geospatial location. The coverage of these networks, in general, is not uniform and the location of peers may follow random distribution. The ICT features used to set up the network are lightweight and user friendly, thus, permitting the peers to join the network without the necessity of specialised ICT knowledge. In this perspective it is of increasing interest for HMR activities to elaborate of Personal Weather Station (PWS) networks, capable to provide almost real-time, location aware, weather data. Moreover, different big players of the web arena are now providing world-wide backbones, suitable to present on detailed map location aware information, obtained by mashing up data from different sources. This is the case, for example, with Google Earth and Google Maps. This paper presents the design of a mashup application aimed at aggregating, refining and visualizing near real-time hydro-meteorological datasets. In particular, we focused on the integration of instant precipitation depths, registered either by widespread semi-professional weather stations and official ones. This sort of information has high importance and usefulness in decision support systems and Civil Protection applications. As a significant case study, we analysed the rainfall data observed during the severe flash-flood event of 4 November 2011 over Liguria region, Italy. The joint use of official observation network with PWS networks and meteorological radar allowed for the making of evident finger-like convection structure.

Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease

PubMed Central

Guo, Hao; Zhang, Fan; Chen, Junjie; Xu, Yong; Xiang, Jie

2017-01-01

Exploring functional interactions among various brain regions is helpful for understanding the pathological underpinnings of neurological disorders. Brain networks provide an important representation of those functional interactions, and thus are widely applied in the diagnosis and classification of neurodegenerative diseases. Many mental disorders involve a sharp decline in cognitive ability as a major symptom, which can be caused by abnormal connectivity patterns among several brain regions. However, conventional functional connectivity networks are usually constructed based on pairwise correlations among different brain regions. This approach ignores higher-order relationships, and cannot effectively characterize the high-order interactions of many brain regions working together. Recent neuroscience research suggests that higher-order relationships between brain regions are important for brain network analysis. Hyper-networks have been proposed that can effectively represent the interactions among brain regions. However, this method extracts the local properties of brain regions as features, but ignores the global topology information, which affects the evaluation of network topology and reduces the performance of the classifier. This problem can be compensated by a subgraph feature-based method, but it is not sensitive to change in a single brain region. Considering that both of these feature extraction methods result in the loss of information, we propose a novel machine learning classification method that combines multiple features of a hyper-network based on functional magnetic resonance imaging in Alzheimer's disease. The method combines the brain region features and subgraph features, and then uses a multi-kernel SVM for classification. This retains not only the global topological information, but also the sensitivity to change in a single brain region. To certify the proposed method, 28 normal control subjects and 38 Alzheimer's disease patients were selected to participate in an experiment. The proposed method achieved satisfactory classification accuracy, with an average of 91.60%. The abnormal brain regions included the bilateral precuneus, right parahippocampal gyrus\\hippocampus, right posterior cingulate gyrus, and other regions that are known to be important in Alzheimer's disease. Machine learning classification combining multiple features of a hyper-network of functional magnetic resonance imaging data in Alzheimer's disease obtains better classification performance. PMID:29209156

Two Years Experience With A Broadband Cable Network In An 1100-Bed Hospital

NASA Astrophysics Data System (ADS)

Cahill, Patrick T.; McCarthy, Robert H.; James, R.; Knowles, R.

1985-09-01

Early in 1983, a three-cable broadband network was installed in The New York Hospital-Cornell Medical Center using well-established cable-TV technology. This network was configured in a vertical tree topology. Currently, it extends over thirteen floors vertically and over two city blocks horizontally. It has now survived several major renovations on the various floors of the hospital. This survivability is a result of the siting of the main tree and of the isolation gained for the branches through the strategic placement of amplifiers. This communications system was designed in a modular fashion for later expansion and so that seven types of functions could be supported on the network without the addition of a new functional level disrupting the functions already existing on the system. Thus far, two functions (real-time image consultation and computer sharing) have been implemented, and two other functions (analog image storage and data base management) are in the prototype stage. Perhaps the most significant feature of our experience thus far has been the ease and utility of analog transmission and storage of images. This experience has lead us to postpone and even de-emphasize digital transmission and storage in our future plans.

Chinese Writing of Deaf or Hard-of-Hearing Students and Normal-Hearing Peers from Complex Network Approach.

PubMed

Jin, Huiyuan; Liu, Haitao

2016-01-01

Deaf or hard-of-hearing individuals usually face a greater challenge to learn to write than their normal-hearing counterparts. Due to the limitations of traditional research methods focusing on microscopic linguistic features, a holistic characterization of the writing linguistic features of these language users is lacking. This study attempts to fill this gap by adopting the methodology of linguistic complex networks. Two syntactic dependency networks are built in order to compare the macroscopic linguistic features of deaf or hard-of-hearing students and those of their normal-hearing peers. One is transformed from a treebank of writing produced by Chinese deaf or hard-of-hearing students, and the other from a treebank of writing produced by their Chinese normal-hearing counterparts. Two major findings are obtained through comparison of the statistical features of the two networks. On the one hand, both linguistic networks display small-world and scale-free network structures, but the network of the normal-hearing students' exhibits a more power-law-like degree distribution. Relevant network measures show significant differences between the two linguistic networks. On the other hand, deaf or hard-of-hearing students tend to have a lower language proficiency level in both syntactic and lexical aspects. The rigid use of function words and a lower vocabulary richness of the deaf or hard-of-hearing students may partially account for the observed differences.

An Optimum Space-to-Ground Communication Concept for CubeSat Platform Utilizing NASA Space Network and Near Earth Network

NASA Technical Reports Server (NTRS)

Wong, Yen F.; Kegege, Obadiah; Schaire, Scott H.; Bussey, George; Altunc, Serhat; Zhang, Yuwen; Patel Chitra

2016-01-01

National Aeronautics and Space Administration (NASA) CubeSat missions are expected to grow rapidly in the next decade. Higher data rate CubeSats are transitioning away from Amateur Radio bands to higher frequency bands. A high-level communication architecture for future space-to-ground CubeSat communication was proposed within NASA Goddard Space Flight Center. This architecture addresses CubeSat direct-to-ground communication, CubeSat to Tracking Data Relay Satellite System (TDRSS) communication, CubeSat constellation with Mothership direct-to-ground communication, and CubeSat Constellation with Mothership communication through K-Band Single Access (KSA). A study has been performed to explore this communication architecture, through simulations, analyses, and identifying technologies, to develop the optimum communication concepts for CubeSat communications. This paper presents details of the simulation and analysis that include CubeSat swarm, daughter ship/mother ship constellation, Near Earth Network (NEN) S and X-band direct to ground link, TDRSS Multiple Access (MA) array vs Single Access mode, notional transceiver/antenna configurations, ground asset configurations and Code Division Multiple Access (CDMA) signal trades for daughter ship/mother ship CubeSat constellation inter-satellite cross link. Results of space science X-band 10 MHz maximum achievable data rate study are summarized. CubeSat NEN Ka-Band end-to-end communication analysis is provided. Current CubeSat communication technologies capabilities are presented. Compatibility test of the CubeSat transceiver through NEN and SN is discussed. Based on the analyses, signal trade studies and technology assessments, the desired CubeSat transceiver features and operation concepts for future CubeSat end-to-end communications are derived.

Convergence

NASA Astrophysics Data System (ADS)

Darcie, Thomas E.; Doverspike, Robert; Zirngibl, Martin; Korotky, Steven K.

2005-08-01

Call for Papers: Convergence The Journal of Optical Networking (JON) invites submissions to a special issue on Convergence. Convergence has become a popular theme in telecommunications, one that has broad implications across all segments of the industry. Continual evolution of technology and applications continues to erase lines between traditionally separate lines of business, with dramatic consequences for vendors, service providers, and consumers. Spectacular advances in all layers of optical networking-leading to abundant, dynamic, cost-effective, and reliable wide-area and local-area connections-have been essential drivers of this evolution. As services and networks continue to evolve towards some notion of convergence, the continued role of optical networks must be explored. One vision of convergence renders all information in a common packet (especially IP) format. This vision is driven by the proliferation of data services. For example, time-division multiplexed (TDM) voice becomes VoIP. Analog cable-television signals become MPEG bits streamed to digital set-top boxes. T1 or OC-N private lines migrate to Ethernet virtual private networks (VPNs). All these packets coexist peacefully within a single packet-routing methodology built on an optical transport layer that combines the flexibility and cost of data networks with telecom-grade reliability. While this vision is appealing in its simplicity and shared widely, specifics of implementation raise many challenges and differences of opinion. For example, many seek to expand the role of Ethernet in these transport networks, while massive efforts are underway to make traditional TDM networks more data friendly within an evolved but backward-compatible SDH/SONET (synchronous digital hierarchy and synchronous optical network) multiplexing hierarchy. From this common underlying theme follow many specific instantiations. Examples include the convergence at the physical, logical, and operational levels of voice and data, video and data, private-line and virtual private-line, fixed and mobile, and local and long-haul services. These trends have many consequences for consumers, vendors, and carriers. Faced with large volumes of low-margin data traffic mixed with traditional voice services, the need for capital conservation and operational efficiency drives carriers away from today's separate overlay networks for each service and towards "converged" platforms. For example, cable operators require transport of multiple services over both hybrid fiber coax (HFC) and DWDM transport technologies. Local carriers seek an economical architecture to deliver integrated services on optically enabled broadband-access networks. Services over wireless-access networks must coexist with those from wired networks. In each case, convergence of networks and services inspires an important set of questions and challenges, driven by the need for low cost, operational efficiency, service performance requirements, and optical transport technology options. This Feature Issue explores the various interpretations and implications of network convergence pertinent to optical networking. How does convergence affect the evolution of optical transport-layer and control approaches? Are the implied directions consistent with research vision for optical networks? Substantial challenges remain. Papers are solicited across the broad spectrum of interests. These include, but are not limited to: Architecture, design and performance of optical wide-area-network (WAN), metro, and access networks Integration strategies for multiservice transport platforms Access methods that bridge traditional and emerging services Network signaling and control methodologies All-optical packet routing and switching techniques

Convergence

NASA Astrophysics Data System (ADS)

Darcie, Thomas E.; Doverspike, Robert; Zirngibl, Martin; Korotky, Steven K.

2005-06-01

Call for Papers: Convergence The Journal of Optical Networking (JON) invites submissions to a special issue on Convergence. Convergence has become a popular theme in telecommunications, one that has broad implications across all segments of the industry. Continual evolution of technology and applications continues to erase lines between traditionally separate lines of business, with dramatic consequences for vendors, service providers, and consumers. Spectacular advances in all layers of optical networking-leading to abundant, dynamic, cost-effective, and reliable wide-area and local-area connections-have been essential drivers of this evolution. As services and networks continue to evolve towards some notion of convergence, the continued role of optical networks must be explored. One vision of convergence renders all information in a common packet (especially IP) format. This vision is driven by the proliferation of data services. For example, time-division multiplexed (TDM) voice becomes VoIP. Analog cable-television signals become MPEG bits streamed to digital set-top boxes. T1 or OC-N private lines migrate to Ethernet virtual private networks (VPNs). All these packets coexist peacefully within a single packet-routing methodology built on an optical transport layer that combines the flexibility and cost of data networks with telecom-grade reliability. While this vision is appealing in its simplicity and shared widely, specifics of implementation raise many challenges and differences of opinion. For example, many seek to expand the role of Ethernet in these transport networks, while massive efforts are underway to make traditional TDM networks more data friendly within an evolved but backward-compatible SDH/SONET (synchronous digital hierarchy and synchronous optical network) multiplexing hierarchy. From this common underlying theme follow many specific instantiations. Examples include the convergence at the physical, logical, and operational levels of voice and data, video and data, private-line and virtual private-line, fixed and mobile, and local and long-haul services. These trends have many consequences for consumers, vendors, and carriers. Faced with large volumes of low-margin data traffic mixed with traditional voice services, the need for capital conservation and operational efficiency drives carriers away from today's separate overlay networks for each service and towards "converged" platforms. For example, cable operators require transport of multiple services over both hybrid fiber coax (HFC) and DWDM transport technologies. Local carriers seek an economical architecture to deliver integrated services on optically enabled broadband-access networks. Services over wireless-access networks must coexist with those from wired networks. In each case, convergence of networks and services inspires an important set of questions and challenges, driven by the need for low cost, operational efficiency, service performance requirements, and optical transport technology options. This Feature Issue explores the various interpretations and implications of network convergence pertinent to optical networking. How does convergence affect the evolution of optical transport-layer and control approaches? Are the implied directions consistent with research vision for optical networks? Substantial challenges remain. Papers are solicited across the broad spectrum of interests. These include, but are not limited to: Architecture, design and performance of optical wide-area-network (WAN), metro, and access networks Integration strategies for multiservice transport platforms Access methods that bridge traditional and emerging services Network signaling and control methodologies All-optical packet routing and switching techniques

Convergence

NASA Astrophysics Data System (ADS)

Darcie, Thomas E.; Doverspike, Robert; Zirngibl, Martin; Korotky, Steven K.

2005-05-01

Call for Papers: Convergence The Journal of Optical Networking (JON) invites submissions to a special issue on Convergence. Convergence has become a popular theme in telecommunications, one that has broad implications across all segments of the industry. Continual evolution of technology and applications continues to erase lines between traditionally separate lines of business, with dramatic consequences for vendors, service providers, and consumers. Spectacular advances in all layers of optical networking-leading to abundant, dynamic, cost-effective, and reliable wide-area and local-area connections-have been essential drivers of this evolution. As services and networks continue to evolve towards some notion of convergence, the continued role of optical networks must be explored. One vision of convergence renders all information in a common packet (especially IP) format. This vision is driven by the proliferation of data services. For example, time-division multiplexed (TDM) voice becomes VoIP. Analog cable-television signals become MPEG bits streamed to digital set-top boxes. T1 or OC-N private lines migrate to Ethernet virtual private networks (VPNs). All these packets coexist peacefully within a single packet-routing methodology built on an optical transport layer that combines the flexibility and cost of data networks with telecom-grade reliability. While this vision is appealing in its simplicity and shared widely, specifics of implementation raise many challenges and differences of opinion. For example, many seek to expand the role of Ethernet in these transport networks, while massive efforts are underway to make traditional TDM networks more data friendly within an evolved but backward-compatible SDH/SONET (synchronous digital hierarchy and synchronous optical network) multiplexing hierarchy. From this common underlying theme follow many specific instantiations. Examples include the convergence at the physical, logical, and operational levels of voice and data, video and data, private-line and virtual private-line, fixed and mobile, and local and long-haul services. These trends have many consequences for consumers, vendors, and carriers. Faced with large volumes of low-margin data traffic mixed with traditional voice services, the need for capital conservation and operational efficiency drives carriers away from today's separate overlay networks for each service and towards "converged" platforms. For example, cable operators require transport of multiple services over both hybrid fiber coax (HFC) and DWDM transport technologies. Local carriers seek an economical architecture to deliver integrated services on optically enabled broadband-access networks. Services over wireless-access networks must coexist with those from wired networks. In each case, convergence of networks and services inspires an important set of questions and challenges, driven by the need for low cost, operational efficiency, service performance requirements, and optical transport technology options. This Feature Issue explores the various interpretations and implications of network convergence pertinent to optical networking. How does convergence affect the evolution of optical transport-layer and control approaches? Are the implied directions consistent with research vision for optical networks? Substantial challenges remain. Papers are solicited across the broad spectrum of interests. These include, but are not limited to: Architecture, design and performance of optical wide-area-network (WAN), metro, and access networks Integration strategies for multiservice transport platforms Access methods that bridge traditional and emerging services Network signaling and control methodologies All-optical packet routing and switching techniques

Convergence

NASA Astrophysics Data System (ADS)

Darcie, Thomas E.; Doverspike, Robert; Zirngibl, Martin; Korotky, Steven K.

2005-04-01

Call for Papers: Convergence The Journal of Optical Networking (JON) invites submissions to a special issue on Convergence. Convergence has become a popular theme in telecommunications, one that has broad implications across all segments of the industry. Continual evolution of technology and applications continues to erase lines between traditionally separate lines of business, with dramatic consequences for vendors, service providers, and consumers. Spectacular advances in all layers of optical networking-leading to abundant, dynamic, cost-effective, and reliable wide-area and local-area connections-have been essential drivers of this evolution. As services and networks continue to evolve towards some notion of convergence, the continued role of optical networks must be explored. One vision of convergence renders all information in a common packet (especially IP) format. This vision is driven by the proliferation of data services. For example, time-division multiplexed (TDM) voice becomes VoIP. Analog cable-television signals become MPEG bits streamed to digital set-top boxes. T1 or OC-N private lines migrate to Ethernet virtual private networks (VPNs). All these packets coexist peacefully within a single packet-routing methodology built on an optical transport layer that combines the flexibility and cost of data networks with telecom-grade reliability. While this vision is appealing in its simplicity and shared widely, specifics of implementation raise many challenges and differences of opinion. For example, many seek to expand the role of Ethernet in these transport networks, while massive efforts are underway to make traditional TDM networks more data friendly within an evolved but backward-compatible SDH/SONET (synchronous digital hierarchy and synchronous optical network) multiplexing hierarchy. From this common underlying theme follow many specific instantiations. Examples include the convergence at the physical, logical, and operational levels of voice and data, video and data, private-line and virtual private-line, fixed and mobile, and local and long-haul services. These trends have many consequences for consumers, vendors, and carriers. Faced with large volumes of low-margin data traffic mixed with traditional voice services, the need for capital conservation and operational efficiency drives carriers away from today's separate overlay networks for each service and towards "converged" platforms. For example, cable operators require transport of multiple services over both hybrid fiber coax (HFC) and DWDM transport technologies. Local carriers seek an economical architecture to deliver integrated services on optically enabled broadband-access networks. Services over wireless-access networks must coexist with those from wired networks. In each case, convergence of networks and services inspires an important set of questions and challenges, driven by the need for low cost, operational efficiency, service performance requirements, and optical transport technology options. This Feature Issue explores the various interpretations and implications of network convergence pertinent to optical networking. How does convergence affect the evolution of optical transport-layer and control approaches? Are the implied directions consistent with research vision for optical networks? Substantial challenges remain. Papers are solicited across the broad spectrum of interests. These include, but are not limited to: Architecture, design and performance of optical wide-area-network (WAN), metro, and access networks Integration strategies for multiservice transport platforms Access methods that bridge traditional and emerging services Network signaling and control methodologies All-optical packet routing and switching techniques

Programming Wireless Handheld Devices for Applications in Teaching Astronomy

NASA Astrophysics Data System (ADS)

Budiardja, R.; Saranathan, V.; Guidry, M.

2002-12-01

Wireless technology implemented with handheld devices has attractive features because of the potential to access large amounts of data and the prospect of on-the-fly computational analysis from a device that can be carried in a shirt pocket. We shall describe applications of such technology to the general paradigm of making digital wireless connections from the field to upload information and queries to network servers, executing (potentially complex) data analysis and/or database operations on fast network computers, and returning real-time information from this analysis to the handheld device in the field. As illustration, we shall describe several client/server programs that we have written for applications in teaching introductory astronomy. For example, one program allows static and dynamic properties of astronomical objects to be accessed in a remote observation laboratory setting using a digital cell phone or PDA. Another implements interactive quizzing over a cell phone or PDA using a 700-question introductory astronomy quiz database, thus permitting students to study for astronomy quizzes in any environment in which they have a few free minutes and a digital cell phone or wireless PDA. The presentation will include hands-on demonstrations with real devices.

The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption

NASA Astrophysics Data System (ADS)

Efremenko, Vladimir; Belyaevsky, Roman; Skrebneva, Evgeniya

2017-11-01

In article the analysis of electric power consumption and problems of power saving on coal mines are considered. Nowadays the share of conditionally constant costs of electric power for providing safe working conditions underground on coal mines is big. Therefore, the power efficiency of underground coal mining depends on electric power expense of the main technological processes and size of conditionally constant costs. The important direction of increase of power efficiency of coal mining is forecasting of a power consumption and monitoring of electric power expense. One of the main approaches to reducing of electric power costs is increase in accuracy of the enterprise demand in the wholesale electric power market. It is offered to use artificial neural networks to forecasting of day-ahead power consumption with hourly breakdown. At the same time use of neural and indistinct (hybrid) systems on the principles of fuzzy logic, neural networks and genetic algorithms is more preferable. This model allows to do exact short-term forecasts at a small array of input data. A set of the input parameters characterizing mining-and-geological and technological features of the enterprise is offered.

Spatial Relation Predicates in Topographic Feature Semantics

USGS Publications Warehouse

Varanka, Dalia E.; Caro, Holly K.

2013-01-01

Topographic data are designed and widely used for base maps of diverse applications, yet the power of these information sources largely relies on the interpretive skills of map readers and relational database expert users once the data are in map or geographic information system (GIS) form. Advances in geospatial semantic technology offer data model alternatives for explicating concepts and articulating complex data queries and statements. To understand and enrich the vocabulary of topographic feature properties for semantic technology, English language spatial relation predicates were analyzed in three standard topographic feature glossaries. The analytical approach drew from disciplinary concepts in geography, linguistics, and information science. Five major classes of spatial relation predicates were identified from the analysis; representations for most of these are not widely available. The classes are: part-whole (which are commonly modeled throughout semantic and linked-data networks), geometric, processes, human intention, and spatial prepositions. These are commonly found in the ‘real world’ and support the environmental science basis for digital topographical mapping. The spatial relation concepts are based on sets of relation terms presented in this chapter, though these lists are not prescriptive or exhaustive. The results of this study make explicit the concepts forming a broad set of spatial relation expressions, which in turn form the basis for expanding the range of possible queries for topographical data analysis and mapping.

Detection and clustering of features in aerial images by neuron network-based algorithm

NASA Astrophysics Data System (ADS)

Vozenilek, Vit

2015-12-01

The paper presents the algorithm for detection and clustering of feature in aerial photographs based on artificial neural networks. The presented approach is not focused on the detection of specific topographic features, but on the combination of general features analysis and their use for clustering and backward projection of clusters to aerial image. The basis of the algorithm is a calculation of the total error of the network and a change of weights of the network to minimize the error. A classic bipolar sigmoid was used for the activation function of the neurons and the basic method of backpropagation was used for learning. To verify that a set of features is able to represent the image content from the user's perspective, the web application was compiled (ASP.NET on the Microsoft .NET platform). The main achievements include the knowledge that man-made objects in aerial images can be successfully identified by detection of shapes and anomalies. It was also found that the appropriate combination of comprehensive features that describe the colors and selected shapes of individual areas can be useful for image analysis.

SINET3: advanced optical and IP hybrid network

NASA Astrophysics Data System (ADS)

Urushidani, Shigeo

2007-11-01

This paper introduces the new Japanese academic backbone network called SINET3, which has been in full-scale operation since June 2007. SINET3 provides a wide variety of network services, such as multi-layer transfer, enriched VPN, enhanced QoS, and layer-1 bandwidth on demand (BoD) services to create an innovative and prolific science infrastructure for more than 700 universities and research institutions. The network applies an advanced hybrid network architecture composed of 75 layer-1 switches and 12 high-performance IP routers to accommodate such diversified services in a single network platform, and provides sufficient bandwidth using Japan's first STM256 (40 Gbps) lines. The network adopts lots of the latest networking technologies, such as next-generation SDH (VCAT/GFP/LCAS), GMPLS, advanced MPLS, and logical-router technologies, for high network convergence, flexible resource assignment, and high service availability. This paper covers the network services, network design, and networking technologies of SINET3.

Deep-learning derived features for lung nodule classification with limited datasets

NASA Astrophysics Data System (ADS)

Thammasorn, P.; Wu, W.; Pierce, L. A.; Pipavath, S. N.; Lampe, P. D.; Houghton, A. M.; Haynor, D. R.; Chaovalitwongse, W. A.; Kinahan, P. E.

2018-02-01

Only a few percent of indeterminate nodules found in lung CT images are cancer. However, enabling earlier diagnosis is important to avoid invasive procedures or long-time surveillance to those benign nodules. We are evaluating a classification framework using radiomics features derived with a machine learning approach from a small data set of indeterminate CT lung nodule images. We used a retrospective analysis of 194 cases with pulmonary nodules in the CT images with or without contrast enhancement from lung cancer screening clinics. The nodules were contoured by a radiologist and texture features of the lesion were calculated. In addition, sematic features describing shape were categorized. We also explored a Multiband network, a feature derivation path that uses a modified convolutional neural network (CNN) with a Triplet Network. This was trained to create discriminative feature representations useful for variable-sized nodule classification. The diagnostic accuracy was evaluated for multiple machine learning algorithms using texture, shape, and CNN features. In the CT contrast-enhanced group, the texture or semantic shape features yielded an overall diagnostic accuracy of 80%. Use of a standard deep learning network in the framework for feature derivation yielded features that substantially underperformed compared to texture and/or semantic features. However, the proposed Multiband approach of feature derivation produced results similar in diagnostic accuracy to the texture and semantic features. While the Multiband feature derivation approach did not outperform the texture and/or semantic features, its equivalent performance indicates promise for future improvements to increase diagnostic accuracy. Importantly, the Multiband approach adapts readily to different size lesions without interpolation, and performed well with relatively small amount of training data.

NAFFS: network attached flash file system for cloud storage on portable consumer electronics

NASA Astrophysics Data System (ADS)

Han, Lin; Huang, Hao; Xie, Changsheng

Cloud storage technology has become a research hotspot in recent years, while the existing cloud storage services are mainly designed for data storage needs with stable high speed Internet connection. Mobile Internet connections are often unstable and the speed is relatively low. These native features of mobile Internet limit the use of cloud storage in portable consumer electronics. The Network Attached Flash File System (NAFFS) presented the idea of taking the portable device built-in NAND flash memory as the front-end cache of virtualized cloud storage device. Modern portable devices with Internet connection have built-in more than 1GB NAND Flash, which is quite enough for daily data storage. The data transfer rate of NAND flash device is much higher than mobile Internet connections[1], and its non-volatile feature makes it very suitable as the cache device of Internet cloud storage on portable device, which often have unstable power supply and intermittent Internet connection. In the present work, NAFFS is evaluated with several benchmarks, and its performance is compared with traditional network attached file systems, such as NFS. Our evaluation results indicate that the NAFFS achieves an average accessing speed of 3.38MB/s, which is about 3 times faster than directly accessing cloud storage by mobile Internet connection， and offers a more stable interface than that of directly using cloud storage API. Unstable Internet connection and sudden power off condition are tolerable, and no data in cache will be lost in such situation.

Automatic Road Sign Inventory Using Mobile Mapping Systems

NASA Astrophysics Data System (ADS)

Soilán, M.; Riveiro, B.; Martínez-Sánchez, J.; Arias, P.

2016-06-01

The periodic inspection of certain infrastructure features plays a key role for road network safety and preservation, and for developing optimal maintenance planning that minimize the life-cycle cost of the inspected features. Mobile Mapping Systems (MMS) use laser scanner technology in order to collect dense and precise three-dimensional point clouds that gather both geometric and radiometric information of the road network. Furthermore, time-stamped RGB imagery that is synchronized with the MMS trajectory is also available. In this paper a methodology for the automatic detection and classification of road signs from point cloud and imagery data provided by a LYNX Mobile Mapper System is presented. First, road signs are detected in the point cloud. Subsequently, the inventory is enriched with geometrical and contextual data such as orientation or distance to the trajectory. Finally, semantic content is given to the detected road signs. As point cloud resolution is insufficient, RGB imagery is used projecting the 3D points in the corresponding images and analysing the RGB data within the bounding box defined by the projected points. The methodology was tested in urban and road environments in Spain, obtaining global recall results greater than 95%, and F-score greater than 90%. In this way, inventory data is obtained in a fast, reliable manner, and it can be applied to improve the maintenance planning of the road network, or to feed a Spatial Information System (SIS), thus, road sign information can be available to be used in a Smart City context.

A network model of the interbank market

NASA Astrophysics Data System (ADS)

Li, Shouwei; He, Jianmin; Zhuang, Yaming

2010-12-01

This work introduces a network model of an interbank market based on interbank credit lending relationships. It generates some network features identified through empirical analysis. The critical issue to construct an interbank network is to decide the edges among banks, which is realized in this paper based on the interbank’s degree of trust. Through simulation analysis of the interbank network model, some typical structural features are identified in our interbank network, which are also proved to exist in real interbank networks. They are namely, a low clustering coefficient and a relatively short average path length, community structures, and a two-power-law distribution of out-degree and in-degree.

Characterizing core-periphery structure of complex network by h-core and fingerprint curve

NASA Astrophysics Data System (ADS)

Li, Simon S.; Ye, Adam Y.; Qi, Eric P.; Stanley, H. Eugene; Ye, Fred Y.

2018-02-01

It is proposed that the core-periphery structure of complex networks can be simulated by h-cores and fingerprint curves. While the features of core structure are characterized by h-core, the features of periphery structure are visualized by rose or spiral curve as the fingerprint curve linking to entire-network parameters. It is suggested that a complex network can be approached by h-core and rose curves as the first-order Fourier-approach, where the core-periphery structure is characterized by five parameters: network h-index, network radius, degree power, network density and average clustering coefficient. The simulation looks Fourier-like analysis.

Creative Speech Technology: editorial introduction to this special issue.

PubMed

Edwards, Alistair D N; Newell, Christopher

2013-10-01

CreST is the Creative Speech Technology Network, a research network which brought together people from a wide variety of backgrounds spanning arts technology and beyond. The papers in this volume represent some of the outcomes of that collaboration. This editorial introduces the background of the network and each of the papers. In conclusion we demonstrate that this work helped to realize many of the objectives of the network.

Research and realization implementation of monitor technology on illegal external link of classified computer

NASA Astrophysics Data System (ADS)

Zhang, Hong

2017-06-01

In recent years, with the continuous development and application of network technology, network security has gradually entered people's field of vision. The host computer network external network of violations is an important reason for the threat of network security. At present, most of the work units have a certain degree of attention to network security, has taken a lot of means and methods to prevent network security problems such as the physical isolation of the internal network, install the firewall at the exit. However, these measures and methods to improve network security are often not comply with the safety rules of human behavior damage. For example, the host to wireless Internet access and dual-network card to access the Internet, inadvertently formed a two-way network of external networks and computer connections [1]. As a result, it is possible to cause some important documents and confidentiality leak even in the the circumstances of user unaware completely. Secrecy Computer Violation Out-of-band monitoring technology can largely prevent the violation by monitoring the behavior of the offending connection. In this paper, we mainly research and discuss the technology of secret computer monitoring.

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

Joe Mambretti

This is the summary report of the third annual Optical Networking Testbed Workshop (ONT3), which brought together leading members of the international advanced research community to address major challenges in creating next generation communication services and technologies. Networking research and development (R&D) communities throughout the world continue to discover new methods and technologies that are enabling breakthroughs in advanced communications. These discoveries are keystones for building the foundation of the future economy, which requires the sophisticated management of extremely large qualities of digital information through high performance communications. This innovation is made possible by basic research and experiments within laboratoriesmore » and on specialized testbeds. Initial network research and development initiatives are driven by diverse motives, including attempts to solve existing complex problems, the desire to create powerful new technologies that do not exist using traditional methods, and the need to create tools to address specific challenges, including those mandated by large scale science or government agency mission agendas. Many new discoveries related to communications technologies transition to wide-spread deployment through standards organizations and commercialization. These transition paths allow for new communications capabilities that drive many sectors of the digital economy. In the last few years, networking R&D has increasingly focused on advancing multiple new capabilities enabled by next generation optical networking. Both US Federal networking R&D and other national R&D initiatives, such as those organized by the National Institute of Information and Communications Technology (NICT) of Japan are creating optical networking technologies that allow for new, powerful communication services. Among the most promising services are those based on new types of multi-service or hybrid networks, which use new optical networking technologies. Several years ago, when many of these optical networking research topics were first being investigated, they were the subject of controversial debate. The new techniques challenged many long-held concepts related to architecture and technology. However, today all major networking organizations are transitioning toward infrastructure that incorporates these new concepts. This progress has been assisted through the series of Optical Networking Testbed Workshops (ONT). The first (ONT1) outlined a general framework of key issues and topics and developed a series of recommendations (www.nren.nasa.gov/workshop7). The second (ONT2) developed a common vision of optical network technologies, services, infrastructure, and organizations (www.nren.nasa.gov/workshop8). Processes that allow for a common vision encourage widespread deployment of these types of resources among advanced networking communities. Also, such a shared vision enables key concepts and technologies to migrate from basic research testbeds to wider networking communities. The ONT-3 workshop built on these earlier activities by expanding discussion to include additional considerations of the international interoperability and of greater impact of optical networking technology on networking in general. In accordance with this recognition, the workshop confirmed that future-oriented research and development is indispensable to fundamentally change the current Internet architecture to create a global network incorporating completely new concepts. The workshop also recognized that the first priority to allow for this progress is basic research and development, including international collaborative activities, which are important for the global realization of interoperability of a new generation architecture.« less