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Sample records for algorithms network architectures

  1. Visual pattern recognition network: its training algorithm and its optoelectronic architecture

    NASA Astrophysics Data System (ADS)

    Wang, Ning; Liu, Liren

    1996-07-01

    A visual pattern recognition network and its training algorithm are proposed. The network constructed of a one-layer morphology network and a two-layer modified Hamming net. This visual network can implement invariant pattern recognition with respect to image translation and size projection. After supervised learning takes place, the visual network extracts image features and classifies patterns much the same as living beings do. Moreover we set up its optoelectronic architecture for real-time pattern recognition.

  2. An Integrated Architecture and Feature Selection Algorithm for Radial Basis Neural Networks

    DTIC Science & Technology

    2002-03-01

    The research contribution of this thesis is the first known integrated architecture and feature selection algorithm for Radial Basis Neural Networks (RBNNs...Additionally, this thesis compares three different classification techniques, Discriminant Analysis (DA), Feed-Forward Neural Networks (FFN) and RBNNs against

  3. Hybrid RF and Digital Beamformer for Cellular Networks: Algorithms, Microwave Architectures, and Measurements

    NASA Astrophysics Data System (ADS)

    Venkateswaran, Vijay; Pivit, Florian; Guan, Lei

    2016-07-01

    Modern wireless communication networks, particularly cellular networks utilize multiple antennas to improve the capacity and signal coverage. In these systems, typically an active transceiver is connected to each antenna. However, this one-to-one mapping between transceivers and antennas will dramatically increase the cost and complexity of a large phased antenna array system. In this paper, firstly we propose a \\emph{partially adaptive} beamformer architecture where a reduced number of transceivers with a digital beamformer (DBF) is connected to an increased number of antennas through an RF beamforming network (RFBN). Then, based on the proposed architecture, we present a methodology to derive the minimum number of transceivers that are required for marco-cell and small-cell base stations, respectively. Subsequently, in order to achieve optimal beampatterns with given cellular standard requirements and RF operational constraints, we propose efficient algorithms to jointly design DBF and RFBN. Starting from the proposed algorithms, we specify generic microwave RFBNs for optimal marco-cell and small-cell networks. In order to verify the proposed approaches, we compare the performance of RFBN using simulations and anechoic chamber measurements. Experimental measurement results confirm the robustness and performance of the proposed hybrid DBF-RFBN concept eventually ensuring that theoretical multi-antenna capacity and coverage are achieved at a little incremental cost.

  4. Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

    NASA Technical Reports Server (NTRS)

    Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland

    1998-01-01

    Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

  5. Irregular Applications: Architectures & Algorithms

    SciTech Connect

    Feo, John T.; Villa, Oreste; Tumeo, Antonino; Secchi, Simone

    2012-02-06

    Irregular applications are characterized by irregular data structures, control and communication patterns. Novel irregular high performance applications which deal with large data sets and require have recently appeared. Unfortunately, current high performance systems and software infrastructures executes irregular algorithms poorly. Only coordinated efforts by end user, area specialists and computer scientists that consider both the architecture and the software stack may be able to provide solutions to the challenges of modern irregular applications.

  6. Information network architectures

    NASA Technical Reports Server (NTRS)

    Murray, N. D.

    1985-01-01

    Graphs, charts, diagrams and outlines of information relative to information network architectures for advanced aerospace missions, such as the Space Station, are presented. Local area information networks are considered a likely technology solution. The principle needs for the network are listed.

  7. Neural network and fuzzy logic based secondary cells charging algorithm development and the controller architecture for implementation

    NASA Astrophysics Data System (ADS)

    Ullah, Muhammed Zafar

    Neural Network and Fuzzy Logic are the two key technologies that have recently received growing attention in solving real world, nonlinear, time variant problems. Because of their learning and/or reasoning capabilities, these techniques do not need a mathematical model of the system, which may be difficult, if not impossible, to obtain for complex systems. One of the major problems in portable or electric vehicle world is secondary cell charging, which shows non-linear characteristics. Portable-electronic equipment, such as notebook computers, cordless and cellular telephones and cordless-electric lawn tools use batteries in increasing numbers. These consumers demand fast charging times, increased battery lifetime and fuel gauge capabilities. All of these demands require that the state-of-charge within a battery be known. Charging secondary cells Fast is a problem, which is difficult to solve using conventional techniques. Charge control is important in fast charging, preventing overcharging and improving battery life. This research work provides a quick and reliable approach to charger design using Neural-Fuzzy technology, which learns the exact battery charging characteristics. Neural-Fuzzy technology is an intelligent combination of neural net with fuzzy logic that learns system behavior by using system input-output data rather than mathematical modeling. The primary objective of this research is to improve the secondary cell charging algorithm and to have faster charging time based on neural network and fuzzy logic technique. Also a new architecture of a controller will be developed for implementing the charging algorithm for the secondary battery.

  8. Inter-Vehicular Ad Hoc Networks: From the Ground Truth to Algorithm Design and Testbed Architecture

    ERIC Educational Resources Information Center

    Giordano, Eugenio

    2011-01-01

    Many of the devices we interact with on a daily basis are currently equipped with wireless connectivity. Soon this will be extended to the vehicles we drive/ride every day. Wirelessly connected vehicles will form a new kind of network that will enable a wide set of innovative applications ranging from enhanced safety to entertainment. To…

  9. Architectural Implications for Spatial Object Association Algorithms

    SciTech Connect

    Kumar, V S; Kurc, T; Saltz, J; Abdulla, G; Kohn, S R; Matarazzo, C

    2009-01-29

    Spatial object association, also referred to as cross-match of spatial datasets, is the problem of identifying and comparing objects in two or more datasets based on their positions in a common spatial coordinate system. In this work, we evaluate two crossmatch algorithms that are used for astronomical sky surveys, on the following database system architecture configurations: (1) Netezza Performance Server R, a parallel database system with active disk style processing capabilities, (2) MySQL Cluster, a high-throughput network database system, and (3) a hybrid configuration consisting of a collection of independent database system instances with data replication support. Our evaluation provides insights about how architectural characteristics of these systems affect the performance of the spatial crossmatch algorithms. We conducted our study using real use-case scenarios borrowed from a large-scale astronomy application known as the Large Synoptic Survey Telescope (LSST).

  10. Hybrid architecture for building secure sensor networks

    NASA Astrophysics Data System (ADS)

    Owens, Ken R., Jr.; Watkins, Steve E.

    2012-04-01

    Sensor networks have various communication and security architectural concerns. Three approaches are defined to address these concerns for sensor networks. The first area is the utilization of new computing architectures that leverage embedded virtualization software on the sensor. Deploying a small, embedded virtualization operating system on the sensor nodes that is designed to communicate to low-cost cloud computing infrastructure in the network is the foundation to delivering low-cost, secure sensor networks. The second area focuses on securing the sensor. Sensor security components include developing an identification scheme, and leveraging authentication algorithms and protocols that address security assurance within the physical, communication network, and application layers. This function will primarily be accomplished through encrypting the communication channel and integrating sensor network firewall and intrusion detection/prevention components to the sensor network architecture. Hence, sensor networks will be able to maintain high levels of security. The third area addresses the real-time and high priority nature of the data that sensor networks collect. This function requires that a quality-of-service (QoS) definition and algorithm be developed for delivering the right data at the right time. A hybrid architecture is proposed that combines software and hardware features to handle network traffic with diverse QoS requirements.

  11. Airport Surface Network Architecture Definition

    NASA Technical Reports Server (NTRS)

    Nguyen, Thanh C.; Eddy, Wesley M.; Bretmersky, Steven C.; Lawas-Grodek, Fran; Ellis, Brenda L.

    2006-01-01

    Currently, airport surface communications are fragmented across multiple types of systems. These communication systems for airport operations at most airports today are based dedicated and separate architectures that cannot support system-wide interoperability and information sharing. The requirements placed upon the Communications, Navigation, and Surveillance (CNS) systems in airports are rapidly growing and integration is urgently needed if the future vision of the National Airspace System (NAS) and the Next Generation Air Transportation System (NGATS) 2025 concept are to be realized. To address this and other problems such as airport surface congestion, the Space Based Technologies Project s Surface ICNS Network Architecture team at NASA Glenn Research Center has assessed airport surface communications requirements, analyzed existing and future surface applications, and defined a set of architecture functions that will help design a scalable, reliable and flexible surface network architecture to meet the current and future needs of airport operations. This paper describes the systems approach or methodology to networking that was employed to assess airport surface communications requirements, analyze applications, and to define the surface network architecture functions as the building blocks or components of the network. The systems approach used for defining these functions is relatively new to networking. It is viewing the surface network, along with its environment (everything that the surface network interacts with or impacts), as a system. Associated with this system are sets of services that are offered by the network to the rest of the system. Therefore, the surface network is considered as part of the larger system (such as the NAS), with interactions and dependencies between the surface network and its users, applications, and devices. The surface network architecture includes components such as addressing/routing, network management, network

  12. Optical Neural Network Classifier Architectures

    DTIC Science & Technology

    1998-04-01

    We present an adaptive opto-electronic neural network hardware architecture capable of exploiting parallel optics to realize real-time processing and...function neural network based on a previously demonstrated binary-input version. The greyscale-input capability broadens the range of applications for...a reduced feature set of multiwavelet images to improve training times and discrimination capability of the neural network . The design uses a joint

  13. A Parallel Rendering Algorithm for MIMD Architectures

    NASA Technical Reports Server (NTRS)

    Crockett, Thomas W.; Orloff, Tobias

    1991-01-01

    Applications such as animation and scientific visualization demand high performance rendering of complex three dimensional scenes. To deliver the necessary rendering rates, highly parallel hardware architectures are required. The challenge is then to design algorithms and software which effectively use the hardware parallelism. A rendering algorithm targeted to distributed memory MIMD architectures is described. For maximum performance, the algorithm exploits both object-level and pixel-level parallelism. The behavior of the algorithm is examined both analytically and experimentally. Its performance for large numbers of processors is found to be limited primarily by communication overheads. An experimental implementation for the Intel iPSC/860 shows increasing performance from 1 to 128 processors across a wide range of scene complexities. It is shown that minimal modifications to the algorithm will adapt it for use on shared memory architectures as well.

  14. Algorithms versus architectures for computational chemistry

    NASA Technical Reports Server (NTRS)

    Partridge, H.; Bauschlicher, C. W., Jr.

    1986-01-01

    The algorithms employed are computationally intensive and, as a result, increased performance (both algorithmic and architectural) is required to improve accuracy and to treat larger molecular systems. Several benchmark quantum chemistry codes are examined on a variety of architectures. While these codes are only a small portion of a typical quantum chemistry library, they illustrate many of the computationally intensive kernels and data manipulation requirements of some applications. Furthermore, understanding the performance of the existing algorithm on present and proposed supercomputers serves as a guide for future programs and algorithm development. The algorithms investigated are: (1) a sparse symmetric matrix vector product; (2) a four index integral transformation; and (3) the calculation of diatomic two electron Slater integrals. The vectorization strategies are examined for these algorithms for both the Cyber 205 and Cray XMP. In addition, multiprocessor implementations of the algorithms are looked at on the Cray XMP and on the MIT static data flow machine proposed by DENNIS.

  15. Optical linear algebra processors - Architectures and algorithms

    NASA Technical Reports Server (NTRS)

    Casasent, David

    1986-01-01

    Attention is given to the component design and optical configuration features of a generic optical linear algebra processor (OLAP) architecture, as well as the large number of OLAP architectures, number representations, algorithms and applications encountered in current literature. Number-representation issues associated with bipolar and complex-valued data representations, high-accuracy (including floating point) performance, and the base or radix to be employed, are discussed, together with case studies on a space-integrating frequency-multiplexed architecture and a hybrid space-integrating and time-integrating multichannel architecture.

  16. Data center networks and network architecture

    NASA Astrophysics Data System (ADS)

    Esaki, Hiroshi

    2014-02-01

    This paper discusses and proposes the architectural framework, which is for data center networks. The data center networks require new technical challenges, and it would be good opportunity to change the functions, which are not need in current and future networks. Based on the observation and consideration on data center networks, this paper proposes; (i) Broadcast-free layer 2 network (i.e., emulation of broadcast at the end-node), (ii) Full-mesh point-to-point pipes, and (iii) IRIDES (Invitation Routing aDvertisement for path Engineering System).

  17. Parallel algorithms and architectures for the manipulator inertia matrix

    SciTech Connect

    Amin-Javaheri, M.

    1989-01-01

    Several parallel algorithms and architectures to compute the manipulator inertia matrix in real time are proposed. An O(N) and an O(log{sub 2}N) parallel algorithm based upon recursive computation of the inertial parameters of sets of composite rigid bodies are formulated. One- and two-dimensional systolic architectures are presented to implement the O(N) parallel algorithm. A cube architecture is employed to implement the diagonal element of the inertia matrix in O(log{sub 2}N) time and the upper off-diagonal elements in O(N) time. The resulting K{sub 1}O(N) + K{sub 2}O(log{sub 2}N) parallel algorithm is more efficient for a cube network implementation. All the architectural configurations are based upon a VLSI Robotics Processor exploiting fine-grain parallelism. In evaluation all the architectural configurations, significant performance parameters such as I/O time and idle time due to processor synchronization as well as CPU utilization and on-chip memory size are fully included. The O(N) and O(log{sub 2}N) parallel algorithms adhere to the precedence relationships among the processors. In order to achieve a higher speedup factor; however, parallel algorithms in conjunction with Non-Strict Computational Models are devised to relax interprocess precedence, and as a result, to decrease the effective computational delays. The effectiveness of the Non-strict Computational Algorithms is verified by computer simulations, based on a PUMA 560 robot manipulator. It is demonstrated that a combination of parallel algorithms and architectures results in a very effective approach to achieve real-time response for computing the manipulator inertia matrix.

  18. Sensor Network Architectures for Monitoring Underwater Pipelines

    PubMed Central

    Mohamed, Nader; Jawhar, Imad; Al-Jaroodi, Jameela; Zhang, Liren

    2011-01-01

    This paper develops and compares different sensor network architecture designs that can be used for monitoring underwater pipeline infrastructures. These architectures are underwater wired sensor networks, underwater acoustic wireless sensor networks, RF (Radio Frequency) wireless sensor networks, integrated wired/acoustic wireless sensor networks, and integrated wired/RF wireless sensor networks. The paper also discusses the reliability challenges and enhancement approaches for these network architectures. The reliability evaluation, characteristics, advantages, and disadvantages among these architectures are discussed and compared. Three reliability factors are used for the discussion and comparison: the network connectivity, the continuity of power supply for the network, and the physical network security. In addition, the paper also develops and evaluates a hierarchical sensor network framework for underwater pipeline monitoring. PMID:22346669

  19. Spectral element methods: Algorithms and architectures

    NASA Technical Reports Server (NTRS)

    Fischer, Paul; Ronquist, Einar M.; Dewey, Daniel; Patera, Anthony T.

    1988-01-01

    Spectral element methods are high-order weighted residual techniques for partial differential equations that combine the geometric flexibility of finite element methods with the rapid convergence of spectral techniques. Spectral element methods are described for the simulation of incompressible fluid flows, with special emphasis on implementation of spectral element techniques on medium-grained parallel processors. Two parallel architectures are considered: the first, a commercially available message-passing hypercube system; the second, a developmental reconfigurable architecture based on Geometry-Defining Processors. High parallel efficiency is obtained in hypercube spectral element computations, indicating that load balancing and communication issues can be successfully addressed by a high-order technique/medium-grained processor algorithm-architecture coupling.

  20. Bipartite memory network architectures for parallel processing

    SciTech Connect

    Smith, W.; Kale, L.V. . Dept. of Computer Science)

    1990-01-01

    Parallel architectures are boradly classified as either shared memory or distributed memory architectures. In this paper, the authors propose a third family of architectures, called bipartite memory network architectures. In this architecture, processors and memory modules constitute a bipartite graph, where each processor is allowed to access a small subset of the memory modules, and each memory module allows access from a small set of processors. The architecture is particularly suitable for computations requiring dynamic load balancing. The authors explore the properties of this architecture by examining the Perfect Difference set based topology for the graph. Extensions of this topology are also suggested.

  1. The NASA Space Communications Data Networking Architecture

    NASA Technical Reports Server (NTRS)

    Israel, David J.; Hooke, Adrian J.; Freeman, Kenneth; Rush, John J.

    2006-01-01

    The NASA Space Communications Architecture Working Group (SCAWG) has recently been developing an integrated agency-wide space communications architecture in order to provide the necessary communication and navigation capabilities to support NASA's new Exploration and Science Programs. A critical element of the space communications architecture is the end-to-end Data Networking Architecture, which must provide a wide range of services required for missions ranging from planetary rovers to human spaceflight, and from sub-orbital space to deep space. Requirements for a higher degree of user autonomy and interoperability between a variety of elements must be accommodated within an architecture that necessarily features minimum operational complexity. The architecture must also be scalable and evolvable to meet mission needs for the next 25 years. This paper will describe the recommended NASA Data Networking Architecture, present some of the rationale for the recommendations, and will illustrate an application of the architecture to example NASA missions.

  2. A hierarchical clustering algorithm for MIMD architecture.

    PubMed

    Du, Zhihua; Lin, Feng

    2004-12-01

    Hierarchical clustering is the most often used method for grouping similar patterns of gene expression data. A fundamental problem with existing implementations of this clustering method is the inability to handle large data sets within a reasonable time and memory resources. We propose a parallelized algorithm of hierarchical clustering to solve this problem. Our implementation on a multiple instruction multiple data (MIMD) architecture shows considerable reduction in computational time and inter-node communication overhead, especially for large data sets. We use the standard message passing library, message passing interface (MPI) for any MIMD systems.

  3. Biologically relevant neural network architectures for support vector machines.

    PubMed

    Jändel, Magnus

    2014-01-01

    Neural network architectures that implement support vector machines (SVM) are investigated for the purpose of modeling perceptual one-shot learning in biological organisms. A family of SVM algorithms including variants of maximum margin, 1-norm, 2-norm and ν-SVM is considered. SVM training rules adapted for neural computation are derived. It is found that competitive queuing memory (CQM) is ideal for storing and retrieving support vectors. Several different CQM-based neural architectures are examined for each SVM algorithm. Although most of the sixty-four scanned architectures are unconvincing for biological modeling four feasible candidates are found. The seemingly complex learning rule of a full ν-SVM implementation finds a particularly simple and natural implementation in bisymmetric architectures. Since CQM-like neural structures are thought to encode skilled action sequences and bisymmetry is ubiquitous in motor systems it is speculated that trainable pattern recognition in low-level perception has evolved as an internalized motor programme.

  4. Gossip algorithms in quantum networks

    NASA Astrophysics Data System (ADS)

    Siomau, Michael

    2017-01-01

    Gossip algorithms is a common term to describe protocols for unreliable information dissemination in natural networks, which are not optimally designed for efficient communication between network entities. We consider application of gossip algorithms to quantum networks and show that any quantum network can be updated to optimal configuration with local operations and classical communication. This allows to speed-up - in the best case exponentially - the quantum information dissemination. Irrespective of the initial configuration of the quantum network, the update requiters at most polynomial number of local operations and classical communication.

  5. Quantum perceptron over a field and neural network architecture selection in a quantum computer.

    PubMed

    da Silva, Adenilton José; Ludermir, Teresa Bernarda; de Oliveira, Wilson Rosa

    2016-04-01

    In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of quantum perceptrons. We also present a learning algorithm named Superposition based Architecture Learning algorithm (SAL) that optimizes the neural network weights and architectures. SAL searches for the best architecture in a finite set of neural network architectures with linear time over the number of patterns in the training set. SAL is the first learning algorithm to determine neural network architectures in polynomial time. This speedup is obtained by the use of quantum parallelism and a non-linear quantum operator.

  6. DSP algorithms in FPGA: proposition of a new architecture

    NASA Astrophysics Data System (ADS)

    Kolasinski, Piotr; Zabolotny, Wojciech

    2008-01-01

    This paper presents a new reconfigurable architecture created in FPGA which is optimized for DSP algorithms like digital filters or digital transforms. The architecture tries to combine advantages of typical architectures like DSP processors and datapath architecture, while avoiding their drawbacks. The architecture is built from blocks called Operational Units (OU). Each Operational Unit contains the Control Unit (CU), which controls its operation. The Operational Units may operate in parallel, which shortens the processing time. This structure is also highly flexible, because all OUs may operate independently, executing their own programs. User may customize connections between units and modify architecture by adding new modules.

  7. The Functional Consequences of Mutualistic Network Architecture

    PubMed Central

    Gómez, José M.; Perfectti, Francisco; Jordano, Pedro

    2011-01-01

    The architecture and properties of many complex networks play a significant role in the functioning of the systems they describe. Recently, complex network theory has been applied to ecological entities, like food webs or mutualistic plant-animal interactions. Unfortunately, we still lack an accurate view of the relationship between the architecture and functioning of ecological networks. In this study we explore this link by building individual-based pollination networks from eight Erysimum mediohispanicum (Brassicaceae) populations. In these individual-based networks, each individual plant in a population was considered a node, and was connected by means of undirected links to conspecifics sharing pollinators. The architecture of these unipartite networks was described by means of nestedness, connectivity and transitivity. Network functioning was estimated by quantifying the performance of the population described by each network as the number of per-capita juvenile plants produced per population. We found a consistent relationship between the topology of the networks and their functioning, since variation across populations in the average per-capita production of juvenile plants was positively and significantly related with network nestedness, connectivity and clustering. Subtle changes in the composition of diverse pollinator assemblages can drive major consequences for plant population performance and local persistence through modifications in the structure of the inter-plant pollination networks. PMID:21283583

  8. Network architecture functional description and design

    SciTech Connect

    Stans, L.; Bencoe, M.; Brown, D.; Kelly, S.; Pierson, L.; Schaldach, C.

    1989-05-25

    This report provides a top level functional description and design for the development and implementation of the central network to support the next generation of SNL, Albuquerque supercomputer in a UNIX{reg sign} environment. It describes the network functions and provides an architecture and topology.

  9. A neural network architecture for data classification.

    PubMed

    Lezoray, O

    2001-02-01

    This article aims at showing an architecture of neural networks designed for the classification of data distributed among a high number of classes. A significant gain in the global classification rate can be obtained by using our architecture. This latter is based on a set of several little neural networks, each one discriminating only two classes. The specialization of each neural network simplifies their structure and improves the classification. Moreover, the learning step automatically determines the number of hidden neurons. The discussion is illustrated by tests on databases from the UCI machine learning database repository. The experimental results show that this architecture can achieve a faster learning, simpler neural networks and an improved performance in classification.

  10. Efficient algorithm and systolic architecture for modular division

    NASA Astrophysics Data System (ADS)

    Chen, Chuanpeng; Qin, Zhongping

    2011-06-01

    A new efficient modular division algorithm suitable for systolic implementation and its systolic architecture is proposed in this article. With a new exit condition of while loop and a new updating method of a control variable, the new algorithm reduces the average of iteration numbers by more than 14.3% compared to the algorithm proposed by Chen, Bai and Chen. Based on the new algorithm, we design a fast systolic architecture with an optimised core computing cell. Compared to the architecture proposed by Chen, Bai and Chen, our systolic architecture has reduced the critical path delay by about 18% and the total computational time for one modular division by almost 30%, with the cost of about 1% more cells. Moreover, by the addition of a flag signal and three logic gates, the proposed systolic architecture can also perform Montgomery modular multiplication and a fast unified modular divider/multiplier is realised.

  11. A VLSI architecture for simplified arithmetic Fourier transform algorithm

    NASA Technical Reports Server (NTRS)

    Reed, Irving S.; Shih, Ming-Tang; Truong, T. K.; Hendon, E.; Tufts, D. W.

    1992-01-01

    The arithmetic Fourier transform (AFT) is a number-theoretic approach to Fourier analysis which has been shown to perform competitively with the classical FFT in terms of accuracy, complexity, and speed. Theorems developed in a previous paper for the AFT algorithm are used here to derive the original AFT algorithm which Bruns found in 1903. This is shown to yield an algorithm of less complexity and of improved performance over certain recent AFT algorithms. A VLSI architecture is suggested for this simplified AFT algorithm. This architecture uses a butterfly structure which reduces the number of additions by 25 percent of that used in the direct method.

  12. Satellite ATM Networks: Architectures and Guidelines Developed

    NASA Technical Reports Server (NTRS)

    vonDeak, Thomas C.; Yegendu, Ferit

    1999-01-01

    An important element of satellite-supported asynchronous transfer mode (ATM) networking will involve support for the routing and rerouting of active connections. Work published under the auspices of the Telecommunications Industry Association (http://www.tiaonline.org), describes basic architectures and routing protocol issues for satellite ATM (SATATM) networks. The architectures and issues identified will serve as a basis for further development of technical specifications for these SATATM networks. Three ATM network architectures for bent pipe satellites and three ATM network architectures for satellites with onboard ATM switches were developed. The architectures differ from one another in terms of required level of mobility, supported data rates, supported terrestrial interfaces, and onboard processing and switching requirements. The documentation addresses low-, middle-, and geosynchronous-Earth-orbit satellite configurations. The satellite environment may require real-time routing to support the mobility of end devices and nodes of the ATM network itself. This requires the network to be able to reroute active circuits in real time. In addition to supporting mobility, rerouting can also be used to (1) optimize network routing, (2) respond to changing quality-of-service requirements, and (3) provide a fault tolerance mechanism. Traffic management and control functions are necessary in ATM to ensure that the quality-of-service requirements associated with each connection are not violated and also to provide flow and congestion control functions. Functions related to traffic management were identified and described. Most of these traffic management functions will be supported by on-ground ATM switches, but in a hybrid terrestrial-satellite ATM network, some of the traffic management functions may have to be supported by the onboard satellite ATM switch. Future work is planned to examine the tradeoffs of placing traffic management functions onboard a satellite as

  13. Routing architecture and security for airborne networks

    NASA Astrophysics Data System (ADS)

    Deng, Hongmei; Xie, Peng; Li, Jason; Xu, Roger; Levy, Renato

    2009-05-01

    Airborne networks are envisioned to provide interconnectivity for terrestial and space networks by interconnecting highly mobile airborne platforms. A number of military applications are expected to be used by the operator, and all these applications require proper routing security support to establish correct route between communicating platforms in a timely manner. As airborne networks somewhat different from traditional wired and wireless networks (e.g., Internet, LAN, WLAN, MANET, etc), security aspects valid in these networks are not fully applicable to airborne networks. Designing an efficient security scheme to protect airborne networks is confronted with new requirements. In this paper, we first identify a candidate routing architecture, which works as an underlying structure for our proposed security scheme. And then we investigate the vulnerabilities and attack models against routing protocols in airborne networks. Based on these studies, we propose an integrated security solution to address routing security issues in airborne networks.

  14. A security architecture for health information networks.

    PubMed

    Kailar, Rajashekar; Muralidhar, Vinod

    2007-10-11

    Health information network security needs to balance exacting security controls with practicality, and ease of implementation in today's healthcare enterprise. Recent work on 'nationwide health information network' architectures has sought to share highly confidential data over insecure networks such as the Internet. Using basic patterns of health network data flow and trust models to support secure communication between network nodes, we abstract network security requirements to a core set to enable secure inter-network data sharing. We propose a minimum set of security controls that can be implemented without needing major new technologies, but yet realize network security and privacy goals of confidentiality, integrity and availability. This framework combines a set of technology mechanisms with environmental controls, and is shown to be sufficient to counter commonly encountered network security threats adequately.

  15. Architecture of a Personal Network Service Layer

    NASA Astrophysics Data System (ADS)

    Joosten, Rieks; den Hartog, Frank; Selgert, Franklin

    We describe a basic service architecture that extends the currently dominant device-oriented approach of Personal Networks (PNs). It specifies functionality for runtime selection and execution of appropriate service components available in the PN, resulting in a highly dynamic, personalized, and context-aware provisioning of PN services to the user. The architectural model clearly connects the responsibilities of the various business roles with the individual properties (resources) of the PN Entities involved.

  16. Architecture and Connectivity Govern Actin Network Contractility.

    PubMed

    Ennomani, Hajer; Letort, Gaëlle; Guérin, Christophe; Martiel, Jean-Louis; Cao, Wenxiang; Nédélec, François; De La Cruz, Enrique M; Théry, Manuel; Blanchoin, Laurent

    2016-03-07

    Actomyosin contractility plays a central role in a wide range of cellular processes, including the establishment of cell polarity, cell migration, tissue integrity, and morphogenesis during development. The contractile response is variable and depends on actomyosin network architecture and biochemical composition. To determine how this coupling regulates actomyosin-driven contraction, we used a micropatterning method that enables the spatial control of actin assembly. We generated a variety of actin templates and measured how defined actin structures respond to myosin-induced forces. We found that the same actin filament crosslinkers either enhance or inhibit the contractility of a network, depending on the organization of actin within the network. Numerical simulations unified the roles of actin filament branching and crosslinking during actomyosin contraction. Specifically, we introduce the concept of "network connectivity" and show that the contractions of distinct actin architectures are described by the same master curve when considering their degree of connectivity. This makes it possible to predict the dynamic response of defined actin structures to transient changes in connectivity. We propose that, depending on the connectivity and the architecture, network contraction is dominated by either sarcomeric-like or buckling mechanisms. More generally, this study reveals how actin network contractility depends on its architecture under a defined set of biochemical conditions.

  17. Centralized and distributed control architectures under Foundation Fieldbus network.

    PubMed

    Persechini, Maria Auxiliadora Muanis; Jota, Fábio Gonçalves

    2013-01-01

    This paper aims at discussing possible automation and control system architectures based on fieldbus networks in which the controllers can be implemented either in a centralized or in a distributed form. An experimental setup is used to demonstrate some of the addressed issues. The control and automation architecture is composed of a supervisory system, a programmable logic controller and various other devices connected to a Foundation Fieldbus H1 network. The procedures used in the network configuration, in the process modelling and in the design and implementation of controllers are described. The specificities of each one of the considered logical organizations are also discussed. Finally, experimental results are analysed using an algorithm for the assessment of control loops to compare the performances between the centralized and the distributed implementations.

  18. A Security Architecture for Health Information Networks

    PubMed Central

    Kailar, Rajashekar

    2007-01-01

    Health information network security needs to balance exacting security controls with practicality, and ease of implementation in today’s healthcare enterprise. Recent work on ‘nationwide health information network’ architectures has sought to share highly confidential data over insecure networks such as the Internet. Using basic patterns of health network data flow and trust models to support secure communication between network nodes, we abstract network security requirements to a core set to enable secure inter-network data sharing. We propose a minimum set of security controls that can be implemented without needing major new technologies, but yet realize network security and privacy goals of confidentiality, integrity and availability. This framework combines a set of technology mechanisms with environmental controls, and is shown to be sufficient to counter commonly encountered network security threats adequately. PMID:18693862

  19. Integrated Network Architecture for NASA's Orion Missions

    NASA Technical Reports Server (NTRS)

    Bhasin, Kul B.; Hayden, Jeffrey L.; Sartwell, Thomas; Miller, Ronald A.; Hudiburg, John J.

    2008-01-01

    NASA is planning a series of short and long duration human and robotic missions to explore the Moon and then Mars. The series of missions will begin with a new crew exploration vehicle (called Orion) that will initially provide crew exchange and cargo supply support to the International Space Station (ISS) and then become a human conveyance for travel to the Moon. The Orion vehicle will be mounted atop the Ares I launch vehicle for a series of pre-launch tests and then launched and inserted into low Earth orbit (LEO) for crew exchange missions to the ISS. The Orion and Ares I comprise the initial vehicles in the Constellation system of systems that later includes Ares V, Earth departure stage, lunar lander, and other lunar surface systems for the lunar exploration missions. These key systems will enable the lunar surface exploration missions to be initiated in 2018. The complexity of the Constellation system of systems and missions will require a communication and navigation infrastructure to provide low and high rate forward and return communication services, tracking services, and ground network services. The infrastructure must provide robust, reliable, safe, sustainable, and autonomous operations at minimum cost while maximizing the exploration capabilities and science return. The infrastructure will be based on a network of networks architecture that will integrate NASA legacy communication, modified elements, and navigation systems. New networks will be added to extend communication, navigation, and timing services for the Moon missions. Internet protocol (IP) and network management systems within the networks will enable interoperability throughout the Constellation system of systems. An integrated network architecture has developed based on the emerging Constellation requirements for Orion missions. The architecture, as presented in this paper, addresses the early Orion missions to the ISS with communication, navigation, and network services over five

  20. Neural network architecture for crossbar switch control

    NASA Technical Reports Server (NTRS)

    Troudet, Terry P.; Walters, Stephen M.

    1991-01-01

    A Hopfield neural network architecture for the real-time control of a crossbar switch for switching packets at maximum throughput is proposed. The network performance and processing time are derived from a numerical simulation of the transitions of the neural network. A method is proposed to optimize electronic component parameters and synaptic connections, and it is fully illustrated by the computer simulation of a VLSI implementation of 4 x 4 neural net controller. The extension to larger size crossbars is demonstrated through the simulation of an 8 x 8 crossbar switch controller, where the performance of the neural computation is discussed in relation to electronic noise and inhomogeneities of network components.

  1. Algorithmes et architectures pour ordinateurs quantiques supraconducteurs

    NASA Astrophysics Data System (ADS)

    Blais, A.

    2003-09-01

    Algorithms and architectures for superconducting quantum computers Since its formulation, information theory was based, implicitly, on the laws of classical physics. Such a formulation is however incomplete because it does not take into account quantum reality. During the last twenty years, expansion of theory information to include quantum effects has known growing interest. The practical realization of a system for quantum data processing system, a quantum computer, presents however many challenges. In this book, we are interested in various aspects of these challenges. We start by presenting algorithmic concepts like optimization of quantum computations and geometric quantum computation. We then consider various designs and aspects of qubits based on Josephson junctions. In particular, an original approach to the interaction between superconducting qubits is presented. This approach is very general since it can be applied to various designs of qubits. Finally, we are interested in read-out of the superconductic flux qubits. The detector suggested here has the advantage that it is possible to uncouple it from the qubit when no measurement is in progress. Depuis sa formulation, la théorie de l'information a été basée, implicitement, sur les lois de la physique classique. Une telle formulation est toutefois incomplète puisqu'elle ne tient pas compte de la réalité quantique. Au cours des vingt dernières années, l'expansion de la théorie de l'information, de façon à englober les effets purement quantiques, a connu un intérêt grandissant. La réalisation d'un système de traitement de l'information quantique, un ordinateur quantique, présente toutefois de nombreux défis. Dans cet ouvrage, on s'intéresse à différents aspects concernant ces défis. On commence par présenter des concepts algorithmiques comme l'optimisation de calculs quantiques et le calcul quantique géométrique. Par la suite, on s'intéresse à différents designs et aspects de l

  2. Sensor network algorithms and applications.

    PubMed

    Trigoni, Niki; Krishnamachari, Bhaskar

    2012-01-13

    A sensor network is a collection of nodes with processing, communication and sensing capabilities deployed in an area of interest to perform a monitoring task. There has now been about a decade of very active research in the area of sensor networks, with significant accomplishments made in terms of both designing novel algorithms and building exciting new sensing applications. This Theme Issue provides a broad sampling of the central challenges and the contributions that have been made towards addressing these challenges in the field, and illustrates the pervasive and central role of sensor networks in monitoring human activities and the environment.

  3. Satellite Networks: Architectures, Applications, and Technologies

    NASA Technical Reports Server (NTRS)

    Bhasin, Kul (Compiler)

    1998-01-01

    Since global satellite networks are moving to the forefront in enhancing the national and global information infrastructures due to communication satellites' unique networking characteristics, a workshop was organized to assess the progress made to date and chart the future. This workshop provided the forum to assess the current state-of-the-art, identify key issues, and highlight the emerging trends in the next-generation architectures, data protocol development, communication interoperability, and applications. Presentations on overview, state-of-the-art in research, development, deployment and applications and future trends on satellite networks are assembled.

  4. Architecture for networked electronic patient record systems.

    PubMed

    Takeda, H; Matsumura, Y; Kuwata, S; Nakano, H; Sakamoto, N; Yamamoto, R

    2000-11-01

    There have been two major approaches to the development of networked electronic patient record (EPR) architecture. One uses object-oriented methodologies for constructing the model, which include the GEHR project, Synapses, HL7 RIM and so on. The second approach uses document-oriented methodologies, as applied in examples of HL7 PRA. It is practically beneficial to take the advantages of both approaches and to add solution technologies for network security such as PKI. In recognition of the similarity with electronic commerce, a certificate authority as a trusted third party will be organised for establishing networked EPR system. This paper describes a Japanese functional model that has been developed, and proposes a document-object-oriented architecture, which is-compared with other existing models.

  5. Fast notification architecture for wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Lee, Dong-Hahk

    2013-03-01

    In an emergency, since it is vital to transmit the message to the users immediately after analysing the data to prevent disaster, this article presents the deployment of a fast notification architecture for a wireless sensor network. The sensor nodes of the proposed architecture can monitor an emergency situation periodically and transmit the sensing data, immediately to the sink node. We decide on the grade of fire situation according to the decision rule using the sensing values of temperature, CO, smoke density and temperature increasing rate. On the other hand, to estimate the grade of air pollution, the sensing data, such as dust, formaldehyde, NO2, CO2, is applied to the given knowledge model. Since the sink node in the architecture has a ZigBee interface, it can transmit the alert messages in real time according to analysed results received from the host server to the terminals equipped with a SIM card-type ZigBee module. Also, the host server notifies the situation to the registered users who have cellular phone through short message service server of the cellular network. Thus, the proposed architecture can adapt an emergency situation dynamically compared to the conventional architecture using video processing. In the testbed, after generating air pollution and fire data, the terminal receives the message in less than 3 s. In the test results, this system can also be applied to buildings and public areas where many people gather together, to prevent unexpected disasters in urban settings.

  6. An Evolutionary Optimization Framework for Neural Networks and Neuromorphic Architectures

    SciTech Connect

    Schuman, Catherine D; Plank, James; Disney, Adam; Reynolds, John

    2016-01-01

    As new neural network and neuromorphic architectures are being developed, new training methods that operate within the constraints of the new architectures are required. Evolutionary optimization (EO) is a convenient training method for new architectures. In this work, we review a spiking neural network architecture and a neuromorphic architecture, and we describe an EO training framework for these architectures. We present the results of this training framework on four classification data sets and compare those results to other neural network and neuromorphic implementations. We also discuss how this EO framework may be extended to other architectures.

  7. LINCS: Livermore's network architecture. [Octopus computing network

    SciTech Connect

    Fletcher, J.G.

    1982-01-01

    Octopus, a local computing network that has been evolving at the Lawrence Livermore National Laboratory for over fifteen years, is currently undergoing a major revision. The primary purpose of the revision is to consolidate and redefine the variety of conventions and formats, which have grown up over the years, into a single standard family of protocols, the Livermore Interactive Network Communication Standard (LINCS). This standard treats the entire network as a single distributed operating system such that access to a computing resource is obtained in a single way, whether that resource is local (on the same computer as the accessing process) or remote (on another computer). LINCS encompasses not only communication but also such issues as the relationship of customer to server processes and the structure, naming, and protection of resources. The discussion includes: an overview of the Livermore user community and computing hardware, the functions and structure of each of the seven layers of LINCS protocol, the reasons why we have designed our own protocols and why we are dissatisfied by the directions that current protocol standards are taking.

  8. Belief network algorithms: A study of performance

    SciTech Connect

    Jitnah, N.

    1996-12-31

    This abstract gives an overview of the work. We present a survey of Belief Network algorithms and propose a domain characterization system to be used as a basis for algorithm comparison and for predicting algorithm performance.

  9. Reconfigurable materials: Algorithm for architectural origami

    NASA Astrophysics Data System (ADS)

    Paik, Jamie

    2017-01-01

    An algorithm has been developed allowing the rational design of origami-inspired materials that can be rearranged to change their properties. This might open the way to strategies for making reconfigurable robots. See Article p.347

  10. Architecture for a Truly Integrated Defense Network

    DTIC Science & Technology

    2004-06-01

    objectives. 7. All element-specific processing must be performed at the originating elements and not at the recipients. The TCN architecture...and wireless communication links. To minimize both data distribution on the network, as well as the processing requirements of participating systems...users receive the amount and type of track data they request or types of data for which they register and are approved. Processing load is reduced

  11. Stereoscopic depth perception for robot vision: algorithms and architectures

    SciTech Connect

    Safranek, R.J.; Kak, A.C.

    1983-01-01

    The implementation of depth perception algorithms for computer vision is considered. In automated manufacturing, depth information is vital for tasks such as path planning and 3-d scene analysis. The presentation begins with a survey of computer algorithms for stereoscopic depth perception. The emphasis is on the Marr-Poggio paradigm of human stereo vision and its computer implementation. In addition, a stereo matching algorithm based on the relaxation labelling technique is examined. A computer architecture designed to efficiently implement stereo matching algorithms, an MIMD array interfaced to a global memory, is presented. 9 references.

  12. Algorithms for tensor network renormalization

    NASA Astrophysics Data System (ADS)

    Evenbly, G.

    2017-01-01

    We discuss in detail algorithms for implementing tensor network renormalization (TNR) for the study of classical statistical and quantum many-body systems. First, we recall established techniques for how the partition function of a 2 D classical many-body system or the Euclidean path integral of a 1 D quantum system can be represented as a network of tensors, before describing how TNR can be implemented to efficiently contract the network via a sequence of coarse-graining transformations. The efficacy of the TNR approach is then benchmarked for the 2 D classical statistical and 1 D quantum Ising models; in particular the ability of TNR to maintain a high level of accuracy over sustained coarse-graining transformations, even at a critical point, is demonstrated.

  13. Projection learning algorithm for threshold - controlled neural networks

    SciTech Connect

    Reznik, A.M.

    1995-03-01

    The projection learning algorithm proposed in [1, 2] and further developed in [3] substantially improves the efficiency of memorizing information and accelerates the learning process in neural networks. This algorithm is compatible with the completely connected neural network architecture (the Hopfield network [4]), but its application to other networks involves a number of difficulties. The main difficulties include constraints on interconnection structure and the need to eliminate the state uncertainty of latent neurons if such are present in the network. Despite the encouraging preliminary results of [3], further extension of the applications of the projection algorithm therefore remains problematic. In this paper, which is a continuation of the work begun in [3], we consider threshold-controlled neural networks. Networks of this type are quite common. They represent the receptor neuron layers in some neurocomputer designs. A similar structure is observed in the lower divisions of biological sensory systems [5]. In multilayer projection neural networks with lateral interconnections, the neuron layers or parts of these layers may also have the structure of a threshold-controlled completely connected network. Here the thresholds are the potentials delivered through the projection connections from other parts of the network. The extension of the projection algorithm to the class of threshold-controlled networks may accordingly prove to be useful both for extending its technical applications and for better understanding of the operation of the nervous system in living organisms.

  14. A modular architecture for transparent computation in recurrent neural networks.

    PubMed

    Carmantini, Giovanni S; Beim Graben, Peter; Desroches, Mathieu; Rodrigues, Serafim

    2017-01-01

    Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata. We then show that the Gödelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space. Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. The resulting networks simulate automata in real-time and are programmed directly, in the absence of network training. To discuss the unique characteristics of the architecture and their consequences, we present two examples: (i) the design of a Central Pattern Generator from a finite-state locomotive controller, and (ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments.

  15. Re-engineering Nascom's network management architecture

    NASA Technical Reports Server (NTRS)

    Drake, Brian C.; Messent, David

    1994-01-01

    The development of Nascom systems for ground communications began in 1958 with Project Vanguard. The low-speed systems (rates less than 9.6 Kbs) were developed following existing standards; but, there were no comparable standards for high-speed systems. As a result, these systems were developed using custom protocols and custom hardware. Technology has made enormous strides since the ground support systems were implemented. Standards for computer equipment, software, and high-speed communications exist and the performance of current workstations exceeds that of the mainframes used in the development of the ground systems. Nascom is in the process of upgrading its ground support systems and providing additional services. The Message Switching System (MSS), Communications Address Processor (CAP), and Multiplexer/Demultiplexer (MDM) Automated Control System (MACS) are all examples of Nascom systems developed using standards such as, X-windows, Motif, and Simple Network Management Protocol (SNMP). Also, the Earth Observing System (EOS) Communications (Ecom) project is stressing standards as an integral part of its network. The move towards standards has produced a reduction in development, maintenance, and interoperability costs, while providing operational quality improvement. The Facility and Resource Manager (FARM) project has been established to integrate the Nascom networks and systems into a common network management architecture. The maximization of standards and implementation of computer automation in the architecture will lead to continued cost reductions and increased operational efficiency. The first step has been to derive overall Nascom requirements and identify the functionality common to all the current management systems. The identification of these common functions will enable the reuse of processes in the management architecture and promote increased use of automation throughout the Nascom network. The MSS, CAP, MACS, and Ecom projects have indicated

  16. A Delay Tolerant Networking Architecture for Airborne Networking

    DTIC Science & Technology

    2010-04-01

    the DTN- based routing protocols. We build a MANET environment model using OPNET Simulator considering those physical parameters. The existing OPNET ...opportunistic. The new model will be the first DTN architecture in OPNET simulator. Our third goal is to design a cross-layer framework that uses DTN...in OPNET network simulator [16]. The network consists of 100 nodes distributed over a 1000 meter by 1000 meter area. All the nodes are configured

  17. Parallel algorithms and architecture for computation of manipulator forward dynamics

    NASA Technical Reports Server (NTRS)

    Fijany, Amir; Bejczy, Antal K.

    1989-01-01

    Parallel computation of manipulator forward dynamics is investigated. Considering three classes of algorithms for the solution of the problem, that is, the O(n), the O(n exp 2), and the O(n exp 3) algorithms, parallelism in the problem is analyzed. It is shown that the problem belongs to the class of NC and that the time and processors bounds are of O(log2/2n) and O(n exp 4), respectively. However, the fastest stable parallel algorithms achieve the computation time of O(n) and can be derived by parallelization of the O(n exp 3) serial algorithms. Parallel computation of the O(n exp 3) algorithms requires the development of parallel algorithms for a set of fundamentally different problems, that is, the Newton-Euler formulation, the computation of the inertia matrix, decomposition of the symmetric, positive definite matrix, and the solution of triangular systems. Parallel algorithms for this set of problems are developed which can be efficiently implemented on a unique architecture, a triangular array of n(n+2)/2 processors with a simple nearest-neighbor interconnection. This architecture is particularly suitable for VLSI and WSI implementations. The developed parallel algorithm, compared to the best serial O(n) algorithm, achieves an asymptotic speedup of more than two orders-of-magnitude in the computation the forward dynamics.

  18. Acoustooptic linear algebra processors - Architectures, algorithms, and applications

    NASA Technical Reports Server (NTRS)

    Casasent, D.

    1984-01-01

    Architectures, algorithms, and applications for systolic processors are described with attention to the realization of parallel algorithms on various optical systolic array processors. Systolic processors for matrices with special structure and matrices of general structure, and the realization of matrix-vector, matrix-matrix, and triple-matrix products and such architectures are described. Parallel algorithms for direct and indirect solutions to systems of linear algebraic equations and their implementation on optical systolic processors are detailed with attention to the pipelining and flow of data and operations. Parallel algorithms and their optical realization for LU and QR matrix decomposition are specifically detailed. These represent the fundamental operations necessary in the implementation of least squares, eigenvalue, and SVD solutions. Specific applications (e.g., the solution of partial differential equations, adaptive noise cancellation, and optimal control) are described to typify the use of matrix processors in modern advanced signal processing.

  19. Navigation Architecture for a Space Mobile Network

    NASA Technical Reports Server (NTRS)

    Valdez, Jennifer E.; Ashman, Benjamin; Gramling, Cheryl; Heckler, Gregory W.; Carpenter, Russell

    2016-01-01

    The Tracking and Data Relay Satellite System (TDRSS) Augmentation Service for Satellites (TASS) is a proposed beacon service to provide a global, space based GPS augmentation service based on the NASA Global Differential GPS (GDGPS) System. The TASS signal will be tied to the GPS time system and usable as an additional ranging and Doppler radiometric source. Additionally, it will provide data vital to autonomous navigation in the near Earth regime, including space weather information, TDRS ephemerides, Earth Orientation Parameters (EOP), and forward commanding capability. TASS benefits include enhancing situational awareness, enabling increased autonomy, and providing near real-time command access for user platforms. As NASA Headquarters' Space Communication and Navigation Office (SCaN) begins to move away from a centralized network architecture and towards a Space Mobile Network (SMN) that allows for user initiated services, autonomous navigation will be a key part of such a system. This paper explores how a TASS beacon service enables the Space Mobile Networking paradigm, what a typical user platform would require, and provides an in-depth analysis of several navigation scenarios and operations concepts. This paper provides an overview of the TASS beacon and its role within the SMN and user community. Supporting navigation analysis is presented for two user mission scenarios: an Earth observing spacecraft in low earth orbit (LEO), and a highly elliptical spacecraft in a lunar resonance orbit. These diverse flight scenarios indicate the breadth of applicability of the TASS beacon for upcoming users within the current network architecture and in the SMN.

  20. NATO Human View Architecture and Human Networks

    NASA Technical Reports Server (NTRS)

    Handley, Holly A. H.; Houston, Nancy P.

    2010-01-01

    The NATO Human View is a system architectural viewpoint that focuses on the human as part of a system. Its purpose is to capture the human requirements and to inform on how the human impacts the system design. The viewpoint contains seven static models that include different aspects of the human element, such as roles, tasks, constraints, training and metrics. It also includes a Human Dynamics component to perform simulations of the human system under design. One of the static models, termed Human Networks, focuses on the human-to-human communication patterns that occur as a result of ad hoc or deliberate team formation, especially teams distributed across space and time. Parameters of human teams that effect system performance can be captured in this model. Human centered aspects of networks, such as differences in operational tempo (sense of urgency), priorities (common goal), and team history (knowledge of the other team members), can be incorporated. The information captured in the Human Network static model can then be included in the Human Dynamics component so that the impact of distributed teams is represented in the simulation. As the NATO militaries transform to a more networked force, the Human View architecture is an important tool that can be used to make recommendations on the proper mix of technological innovations and human interactions.

  1. Algorithm and Architecture Independent Benchmarking with SEAK

    SciTech Connect

    Tallent, Nathan R.; Manzano Franco, Joseph B.; Gawande, Nitin A.; Kang, Seung-Hwa; Kerbyson, Darren J.; Hoisie, Adolfy; Cross, Joseph

    2016-05-23

    Many applications of high performance embedded computing are limited by performance or power bottlenecks. We have designed the Suite for Embedded Applications & Kernels (SEAK), a new benchmark suite, (a) to capture these bottlenecks in a way that encourages creative solutions; and (b) to facilitate rigorous, objective, end-user evaluation for their solutions. To avoid biasing solutions toward existing algorithms, SEAK benchmarks use a mission-centric (abstracted from a particular algorithm) and goal-oriented (functional) specification. To encourage solutions that are any combination of software or hardware, we use an end-user black-box evaluation that can capture tradeoffs between performance, power, accuracy, size, and weight. The tradeoffs are especially informative for procurement decisions. We call our benchmarks future proof because each mission-centric interface and evaluation remains useful despite shifting algorithmic preferences. It is challenging to create both concise and precise goal-oriented specifications for mission-centric problems. This paper describes the SEAK benchmark suite and presents an evaluation of sample solutions that highlights power and performance tradeoffs.

  2. 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.

  3. Probabilistic logic modeling of network reliability for hybrid network architectures

    SciTech Connect

    Wyss, G.D.; Schriner, H.K.; Gaylor, T.R.

    1996-10-01

    Sandia National Laboratories has found that the reliability and failure modes of current-generation network technologies can be effectively modeled using fault tree-based probabilistic logic modeling (PLM) techniques. We have developed fault tree models that include various hierarchical networking technologies and classes of components interconnected in a wide variety of typical and atypical configurations. In this paper we discuss the types of results that can be obtained from PLMs and why these results are of great practical value to network designers and analysts. After providing some mathematical background, we describe the `plug-and-play` fault tree analysis methodology that we have developed for modeling connectivity and the provision of network services in several current- generation network architectures. Finally, we demonstrate the flexibility of the method by modeling the reliability of a hybrid example network that contains several interconnected ethernet, FDDI, and token ring segments. 11 refs., 3 figs., 1 tab.

  4. Self-growing neural network architecture using crisp and fuzzy entropy

    NASA Technical Reports Server (NTRS)

    Cios, Krzysztof J.

    1992-01-01

    The paper briefly describes the self-growing neural network algorithm, CID3, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results for a real-life recognition problem of distinguishing defects in a glass ribbon, and for a benchmark problen of telling two spirals apart are shown and discussed.

  5. Self-growing neural network architecture using crisp and fuzzy entropy

    NASA Technical Reports Server (NTRS)

    Cios, Krzysztof J.

    1992-01-01

    The paper briefly describes the self-growing neural network algorithm, CID2, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results of a real-life recognition problem of distinguishing defects in a glass ribbon and of a benchmark problem of differentiating two spirals are shown and discussed.

  6. Architecture-Aware Algorithms for Scalable Performance and Resilience on Heterogeneous Architectures. Final Report

    SciTech Connect

    Gropp, William D.

    2014-06-23

    With the coming end of Moore's law, it has become essential to develop new algorithms and techniques that can provide the performance needed by demanding computational science applications, especially those that are part of the DOE science mission. This work was part of a multi-institution, multi-investigator project that explored several approaches to develop algorithms that would be effective at the extreme scales and with the complex processor architectures that are expected at the end of this decade. The work by this group developed new performance models that have already helped guide the development of highly scalable versions of an algebraic multigrid solver, new programming approaches designed to support numerical algorithms on heterogeneous architectures, and a new, more scalable version of conjugate gradient, an important algorithm in the solution of very large linear systems of equations.

  7. Scalable Architecture for Multihop Wireless ad Hoc Networks

    NASA Technical Reports Server (NTRS)

    Arabshahi, Payman; Gray, Andrew; Okino, Clayton; Yan, Tsun-Yee

    2004-01-01

    A scalable architecture for wireless digital data and voice communications via ad hoc networks has been proposed. Although the details of the architecture and of its implementation in hardware and software have yet to be developed, the broad outlines of the architecture are fairly clear: This architecture departs from current commercial wireless communication architectures, which are characterized by low effective bandwidth per user and are not well suited to low-cost, rapid scaling in large metropolitan areas. This architecture is inspired by a vision more akin to that of more than two dozen noncommercial community wireless networking organizations established by volunteers in North America and several European countries.

  8. A biomimetic adaptive algorithm and low-power architecture for implantable neural decoders.

    PubMed

    Rapoport, Benjamin I; Wattanapanitch, Woradorn; Penagos, Hector L; Musallam, Sam; Andersen, Richard A; Sarpeshkar, Rahul

    2009-01-01

    Algorithmically and energetically efficient computational architectures that operate in real time are essential for clinically useful neural prosthetic devices. Such devices decode raw neural data to obtain direct control signals for external devices. They can also perform data compression and vastly reduce the bandwidth and consequently power expended in wireless transmission of raw data from implantable brain-machine interfaces. We describe a biomimetic algorithm and micropower analog circuit architecture for decoding neural cell ensemble signals. The decoding algorithm implements a continuous-time artificial neural network, using a bank of adaptive linear filters with kernels that emulate synaptic dynamics. The filters transform neural signal inputs into control-parameter outputs, and can be tuned automatically in an on-line learning process. We provide experimental validation of our system using neural data from thalamic head-direction cells in an awake behaving rat.

  9. Resource utilization model for the algorithm to architecture mapping model

    NASA Technical Reports Server (NTRS)

    Stoughton, John W.; Patel, Rakesh R.

    1993-01-01

    The analytical model for resource utilization and the variable node time and conditional node model for the enhanced ATAMM model for a real-time data flow architecture are presented in this research. The Algorithm To Architecture Mapping Model, ATAMM, is a Petri net based graph theoretic model developed at Old Dominion University, and is capable of modeling the execution of large-grained algorithms on a real-time data flow architecture. Using the resource utilization model, the resource envelope may be obtained directly from a given graph and, consequently, the maximum number of required resources may be evaluated. The node timing diagram for one iteration period may be obtained using the analytical resource envelope. The variable node time model, which describes the change in resource requirement for the execution of an algorithm under node time variation, is useful to expand the applicability of the ATAMM model to heterogeneous architectures. The model also describes a method of detecting the presence of resource limited mode and its subsequent prevention. Graphs with conditional nodes are shown to be reduced to equivalent graphs with time varying nodes and, subsequently, may be analyzed using the variable node time model to determine resource requirements. Case studies are performed on three graphs for the illustration of applicability of the analytical theories.

  10. 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.

  11. Algorithms and Requirements for Measuring Network Bandwidth

    SciTech Connect

    Jin, Guojun

    2002-12-08

    This report unveils new algorithms for actively measuring (not estimating) available bandwidths with very low intrusion, computing cross traffic, thus estimating the physical bandwidth, provides mathematical proof that the algorithms are accurate, and addresses conditions, requirements, and limitations for new and existing algorithms for measuring network bandwidths. The paper also discusses a number of important terminologies and issues for network bandwidth measurement, and introduces a fundamental parameter -Maximum Burst Size that is critical for implementing algorithms based on multiple packets.

  12. Scalable Virtual Network Mapping Algorithm for Internet-Scale Networks

    NASA Astrophysics Data System (ADS)

    Yang, Qiang; Wu, Chunming; Zhang, Min

    The proper allocation of network resources from a common physical substrate to a set of virtual networks (VNs) is one of the key technical challenges of network virtualization. While a variety of state-of-the-art algorithms have been proposed in an attempt to address this issue from different facets, the challenge still remains in the context of large-scale networks as the existing solutions mainly perform in a centralized manner which requires maintaining the overall and up-to-date information of the underlying substrate network. This implies the restricted scalability and computational efficiency when the network scale becomes large. This paper tackles the virtual network mapping problem and proposes a novel hierarchical algorithm in conjunction with a substrate network decomposition approach. By appropriately transforming the underlying substrate network into a collection of sub-networks, the hierarchical virtual network mapping algorithm can be carried out through a global virtual network mapping algorithm (GVNMA) and a local virtual network mapping algorithm (LVNMA) operated in the network central server and within individual sub-networks respectively with their cooperation and coordination as necessary. The proposed algorithm is assessed against the centralized approaches through a set of numerical simulation experiments for a range of network scenarios. The results show that the proposed hierarchical approach can be about 5-20 times faster for VN mapping tasks than conventional centralized approaches with acceptable communication overhead between GVNCA and LVNCA for all examined networks, whilst performs almost as well as the centralized solutions.

  13. An SN Algorithm for Modern Architectures

    SciTech Connect

    Baker, Randal Scott

    2016-08-29

    LANL discrete ordinates transport packages are required to perform large, computationally intensive time-dependent calculations on massively parallel architectures, where even a single such calculation may need many months to complete. While KBA methods scale out well to very large numbers of compute nodes, we are limited by practical constraints on the number of such nodes we can actually apply to any given calculation. Instead, we describe a modified KBA algorithm that allows realization of the reductions in solution time offered by both the current, and future, architectural changes within a compute node.

  14. Network architecture underlying maximal separation of neuronal representations

    PubMed Central

    Jortner, Ron A.

    2011-01-01

    One of the most basic and general tasks faced by all nervous systems is extracting relevant information from the organism's surrounding world. While physical signals available to sensory systems are often continuous, variable, overlapping, and noisy, high-level neuronal representations used for decision-making tend to be discrete, specific, invariant, and highly separable. This study addresses the question of how neuronal specificity is generated. Inspired by experimental findings on network architecture in the olfactory system of the locust, I construct a highly simplified theoretical framework which allows for analytic solution of its key properties. For generalized feed-forward systems, I show that an intermediate range of connectivity values between source- and target-populations leads to a combinatorial explosion of wiring possibilities, resulting in input spaces which are, by their very nature, exquisitely sparsely populated. In particular, connection probability ½, as found in the locust antennal-lobe–mushroom-body circuit, serves to maximize separation of neuronal representations across the target Kenyon cells (KCs), and explains their specific and reliable responses. This analysis yields a function expressing response specificity in terms of lower network parameters; together with appropriate gain control this leads to a simple neuronal algorithm for generating arbitrarily sparse and selective codes and linking network architecture and neural coding. I suggest a straightforward way to construct ecologically meaningful representations from this code. PMID:23316159

  15. 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,…

  16. Algorithms for radio networks with dynamic topology

    NASA Astrophysics Data System (ADS)

    Shacham, Nachum; Ogier, Richard; Rutenburg, Vladislav V.; Garcia-Luna-Aceves, Jose

    1991-08-01

    The objective of this project was the development of advanced algorithms and protocols that efficiently use network resources to provide optimal or nearly optimal performance in future communication networks with highly dynamic topologies and subject to frequent link failures. As reflected by this report, we have achieved our objective and have significantly advanced the state-of-the-art in this area. The research topics of the papers summarized include the following: efficient distributed algorithms for computing shortest pairs of disjoint paths; minimum-expected-delay alternate routing algorithms for highly dynamic unreliable networks; algorithms for loop-free routing; multipoint communication by hierarchically encoded data; efficient algorithms for extracting the maximum information from event-driven topology updates; methods for the neural network solution of link scheduling and other difficult problems arising in communication networks; and methods for robust routing in networks subject to sophisticated attacks.

  17. A biconjugate gradient type algorithm on massively parallel architectures

    NASA Technical Reports Server (NTRS)

    Freund, Roland W.; Hochbruck, Marlis

    1991-01-01

    The biconjugate gradient (BCG) method is the natural generalization of the classical conjugate gradient algorithm for Hermitian positive definite matrices to general non-Hermitian linear systems. Unfortunately, the original BCG algorithm is susceptible to possible breakdowns and numerical instabilities. Recently, Freund and Nachtigal have proposed a novel BCG type approach, the quasi-minimal residual method (QMR), which overcomes the problems of BCG. Here, an implementation is presented of QMR based on an s-step version of the nonsymmetric look-ahead Lanczos algorithm. The main feature of the s-step Lanczos algorithm is that, in general, all inner products, except for one, can be computed in parallel at the end of each block; this is unlike the other standard Lanczos process where inner products are generated sequentially. The resulting implementation of QMR is particularly attractive on massively parallel SIMD architectures, such as the Connection Machine.

  18. Predicting Electrocardiogram and Arterial Blood Pressure Waveforms with Different Echo State Network Architectures

    DTIC Science & Technology

    2014-11-01

    networks were trained to predict an individual’s electrocardiogram ( ECG ) and arterial blood pressure (ABP) waveform data, which can potentially help...various ESN architectures for prediction tasks, and establishes the benefits of using ESN architecture designs for predicting ECG and ABP waveforms...alarms into true alarms and false alarms. These authors then developed an algorithm that classified alarms based on both electrocardiogram ( ECG ) and

  19. Establishment of a Spaceport Network Architecture

    NASA Technical Reports Server (NTRS)

    Larson, Wiley J.; Gill, Tracy R.; Mueller, Robert P.; Brink, Jeffrey S.

    2012-01-01

    Since the beginning of the space age, the main actors in space exploration have been governmental agencies, enabling a privileged access to space, but with very restricted and rare missions. The last decade has seen the rise of space tourism, and the founding of ambitious private space mining companies, showing the beginnings of a new exploration era, that is based on a more generalized and regular access to space and which is not limited to the Earth's vicinity. However, the cost of launching sufficient mass into orbit to sustain these inspiring challenges is prohibitive, and the necessary infrastructures to support these missions is still lacking. To provide easy and affordable access into orbital and deep space destinations, there is the need to create a network of spaceports via specific waypoint locations coupled with the use of natural resources, or In Situ Resource Utilization (ISRU), to provide a more economical solution. As part of the International Space University Space Studies Program 2012, the international and intercultural team of Operations and Service Infrastructure for Space (OASIS) proposes an interdisciplinary answer to the problem of economical space access and transportation. This paper presents a summary of a detailed report [1] of the different phases of a project for developing a network of spaceports throughout the Solar System in a timeframe of 50 years. The requirements, functions, critical technologies and mission architecture of this network of spaceports are outlined in a roadmap of the important steps and phases. The economic and financial aspects are emphasized in order to allow a sustainable development of the network in a public-private partnership via the formation of an International Spaceport Authority (ISPA). The approach includes engineering, scientific, financial, legal, policy, and societal aspects. Team OASIS intends to provide guidelines to make the development of space transportation via a spaceports logistics network

  20. Navigation Architecture For A Space Mobile Network

    NASA Technical Reports Server (NTRS)

    Valdez, Jennifer E.; Ashman, Benjamin; Gramling, Cheryl; Heckler, Gregory W.; Carpenter, Russell

    2016-01-01

    The Tracking and Data Relay Satellite System (TDRSS) Augmentation Service for Satellites (TASS) is a proposed beacon service to provide a global, space-based GPS augmentation service based on the NASA Global Differential GPS (GDGPS) System. The TASS signal will be tied to the GPS time system and usable as an additional ranging and Doppler radiometric source. Additionally, it will provide data vital to autonomous navigation in the near Earth regime, including space weather information, TDRS ephemerides, Earth Orientation Parameters (EOP), and forward commanding capability. TASS benefits include enhancing situational awareness, enabling increased autonomy, and providing near real-time command access for user platforms. As NASA Headquarters Space Communication and Navigation Office (SCaN) begins to move away from a centralized network architecture and towards a Space Mobile Network (SMN) that allows for user initiated services, autonomous navigation will be a key part of such a system. This paper explores how a TASS beacon service enables the Space Mobile Networking paradigm, what a typical user platform would require, and provides an in-depth analysis of several navigation scenarios and operations concepts.

  1. MSAT signalling and network management architectures

    NASA Technical Reports Server (NTRS)

    Garland, Peter; Keelty, J. Malcolm

    1989-01-01

    Spar Aerospace has been active in the design and definition of Mobile Satellite Systems since the mid 1970's. In work sponsored by the Canadian Department of Communications, various payload configurations have evolved. In addressing the payload configuration, the requirements of the mobile user, the service provider and the satellite operator have always been the most important consideration. The current Spar 11 beam satellite design is reviewed, and its capabilities to provide flexibility and potential for network growth within the WARC87 allocations are explored. To enable the full capabilities of the payload to be realized, a large amount of ground based Switching and Network Management infrastructure will be required, when space segment becomes available. Early indications were that a single custom designed Demand Assignment Multiple Access (DAMA) switch should be implemented to provide efficient use of the space segment. As MSAT has evolved into a multiple service concept, supporting many service providers, this architecture should be reviewed. Some possible signalling and Network Management solutions are explored.

  2. Neural Network Classifier Architectures for Phoneme Recognition. CRC Technical Note No. CRC-TN-92-001.

    ERIC Educational Resources Information Center

    Treurniet, William

    A study applied artificial neural networks, trained with the back-propagation learning algorithm, to modelling phonemes extracted from the DARPA TIMIT multi-speaker, continuous speech data base. A number of proposed network architectures were applied to the phoneme classification task, ranging from the simple feedforward multilayer network to more…

  3. Constructing backbone network by using tinker algorithm

    NASA Astrophysics Data System (ADS)

    He, Zhiwei; Zhan, Meng; Wang, Jianxiong; Yao, Chenggui

    2017-01-01

    Revealing how a biological network is organized to realize its function is one of the main topics in systems biology. The functional backbone network, defined as the primary structure of the biological network, is of great importance in maintaining the main function of the biological network. We propose a new algorithm, the tinker algorithm, to determine this core structure and apply it in the cell-cycle system. With this algorithm, the backbone network of the cell-cycle network can be determined accurately and efficiently in various models such as the Boolean model, stochastic model, and ordinary differential equation model. Results show that our algorithm is more efficient than that used in the previous research. We hope this method can be put into practical use in relevant future studies.

  4. Transformation of legacy network management system to service oriented architecture

    NASA Astrophysics Data System (ADS)

    Sathyan, Jithesh; Shenoy, Krishnananda

    2007-09-01

    Service providers today are facing the challenge of operating and maintaining multiple networks, based on multiple technologies. Network Management System (NMS) solutions are being used to manage these networks. However the NMS is tightly coupled with Element or the Core network components. Hence there are multiple NMS solutions for heterogeneous networks. Current network management solutions are targeted at a variety of independent networks. The wide spread popularity of IP Multimedia Subsystem (IMS) is a clear indication that all of these independent networks will be integrated into a single IP-based infrastructure referred to as Next Generation Networks (NGN) in the near future. The services, network architectures and traffic pattern in NGN will dramatically differ from the current networks. The heterogeneity and complexity in NGN including concepts like Fixed Mobile Convergence will bring a number of challenges to network management. The high degree of complexity accompanying the network element technology necessitates network management systems (NMS) which can utilize this technology to provide more service interfaces while hiding the inherent complexity. As operators begin to add new networks and expand existing networks to support new technologies and products, the necessity of scalable, flexible and functionally rich NMS systems arises. Another important factor influencing NMS architecture is mergers and acquisitions among the key vendors. Ease of integration is a key impediment in the traditional hierarchical NMS architecture. These requirements trigger the need for an architectural framework that will address the NGNM (Next Generation Network Management) issues seamlessly. This paper presents a unique perspective of bringing service orientated architecture (SOA) to legacy network management systems (NMS). It advocates a staged approach in transforming a legacy NMS to SOA. The architecture at each stage is detailed along with the technical advantages and

  5. Pinning impulsive control algorithms for complex network

    SciTech Connect

    Sun, Wen; Lü, Jinhu; Chen, Shihua; Yu, Xinghuo

    2014-03-15

    In this paper, we further investigate the synchronization of complex dynamical network via pinning control in which a selection of nodes are controlled at discrete times. Different from most existing work, the pinning control algorithms utilize only the impulsive signals at discrete time instants, which may greatly improve the communication channel efficiency and reduce control cost. Two classes of algorithms are designed, one for strongly connected complex network and another for non-strongly connected complex network. It is suggested that in the strongly connected network with suitable coupling strength, a single controller at any one of the network's nodes can always pin the network to its homogeneous solution. In the non-strongly connected case, the location and minimum number of nodes needed to pin the network are determined by the Frobenius normal form of the coupling matrix. In addition, the coupling matrix is not necessarily symmetric or irreducible. Illustrative examples are then given to validate the proposed pinning impulsive control algorithms.

  6. Fault Tolerant Statistical Signal Processing Algorithms for Parallel Architectures.

    DTIC Science & Technology

    2014-09-26

    AD-fi57 393 FAULT TOLERANT STATISTICAL SIGNAL PROCESSING ALGORITHMS i/i FOR PARALLEL ARCH U) JOHNS HOPKINS UNIV BALTIMORE MD DEPT OF ELECTRICAL...COVERED * ’ Fault Tolerant Statistical Signal Processing Technical A l g o r i t h m s f o r P a r a l l e l A r c h i t e c t u r e s a ._ P E R F O R M I...Identify by block number) , Fault Tolerance, Signal Processing, Parallel Architecture 0 20. ABSTRACT (Continue on reveree side It neceseary and identify by

  7. Modeling cognitive and emotional processes: a novel neural network architecture.

    PubMed

    Khashman, Adnan

    2010-12-01

    In our continuous attempts to model natural intelligence and emotions in machine learning, many research works emerge with different methods that are often driven by engineering concerns and have the common goal of modeling human perception in machines. This paper aims to go further in that direction by investigating the integration of emotion at the structural level of cognitive systems using the novel emotional DuoNeural Network (DuoNN). This network has hidden layer DuoNeurons, where each has two embedded neurons: a dorsal neuron and a ventral neuron for cognitive and emotional data processing, respectively. When input visual stimuli are presented to the DuoNN, the dorsal cognitive neurons process local features while the ventral emotional neurons process the entire pattern. We present the computational model and the learning algorithm of the DuoNN, the input information-cognitive and emotional-parallel streaming method, and a comparison between the DuoNN and a recently developed emotional neural network. Experimental results show that the DuoNN architecture, configuration, and the additional emotional information processing, yield higher recognition rates and faster learning and decision making.

  8. A common network architecture efficiently implements a variety of sparsity-based inference problems.

    PubMed

    Charles, Adam S; Garrigues, Pierre; Rozell, Christopher J

    2012-12-01

    The sparse coding hypothesis has generated significant interest in the computational and theoretical neuroscience communities, but there remain open questions about the exact quantitative form of the sparsity penalty and the implementation of such a coding rule in neurally plausible architectures. The main contribution of this work is to show that a wide variety of sparsity-based probabilistic inference problems proposed in the signal processing and statistics literatures can be implemented exactly in the common network architecture known as the locally competitive algorithm (LCA). Among the cost functions we examine are approximate l(p) norms (0 ≤ p ≤ 2), modified l(p)-norms, block-l1 norms, and reweighted algorithms. Of particular interest is that we show significantly increased performance in reweighted l1 algorithms by inferring all parameters jointly in a dynamical system rather than using an iterative approach native to digital computational architectures.

  9. Space Mobile Network: A Near Earth Communication and Navigation Architecture

    NASA Technical Reports Server (NTRS)

    Israel, Dave J.; Heckler, Greg; Menrad, Robert J.

    2016-01-01

    This paper describes a Space Mobile Network architecture, the result of a recently completed NASA study exploring architectural concepts to produce a vision for the future Near Earth communications and navigation systems. The Space Mobile Network (SMN) incorporates technologies, such as Disruption Tolerant Networking (DTN) and optical communications, and new operations concepts, such as User Initiated Services, to provide user services analogous to a terrestrial smartphone user. The paper will describe the SMN Architecture, envisioned future operations concepts, opportunities for industry and international collaboration and interoperability, and technology development areas and goals.

  10. A Parallel Saturation Algorithm on Shared Memory Architectures

    NASA Technical Reports Server (NTRS)

    Ezekiel, Jonathan; Siminiceanu

    2007-01-01

    Symbolic state-space generators are notoriously hard to parallelize. However, the Saturation algorithm implemented in the SMART verification tool differs from other sequential symbolic state-space generators in that it exploits the locality of ring events in asynchronous system models. This paper explores whether event locality can be utilized to efficiently parallelize Saturation on shared-memory architectures. Conceptually, we propose to parallelize the ring of events within a decision diagram node, which is technically realized via a thread pool. We discuss the challenges involved in our parallel design and conduct experimental studies on its prototypical implementation. On a dual-processor dual core PC, our studies show speed-ups for several example models, e.g., of up to 50% for a Kanban model, when compared to running our algorithm only on a single core.

  11. Memetic algorithm for community detection in networks.

    PubMed

    Gong, Maoguo; Fu, Bao; Jiao, Licheng; Du, Haifeng

    2011-11-01

    Community structure is one of the most important properties in networks, and community detection has received an enormous amount of attention in recent years. Modularity is by far the most used and best known quality function for measuring the quality of a partition of a network, and many community detection algorithms are developed to optimize it. However, there is a resolution limit problem in modularity optimization methods. In this study, a memetic algorithm, named Meme-Net, is proposed to optimize another quality function, modularity density, which includes a tunable parameter that allows one to explore the network at different resolutions. Our proposed algorithm is a synergy of a genetic algorithm with a hill-climbing strategy as the local search procedure. Experiments on computer-generated and real-world networks show the effectiveness and the multiresolution ability of the proposed method.

  12. Security Aspects of an Enterprise-Wide Network Architecture.

    ERIC Educational Resources Information Center

    Loew, Robert; Stengel, Ingo; Bleimann, Udo; McDonald, Aidan

    1999-01-01

    Presents an overview of two projects that concern local area networks and the common point between networks as they relate to network security. Discusses security architectures based on firewall components, packet filters, application gateways, security-management components, an intranet solution, user registration by Web form, and requests for…

  13. An Architecture for Cooperative Localization in Underwater Acoustic Networks

    DTIC Science & Technology

    2015-10-24

    An Architecture for Cooperative Localization in Underwater Acoustic Networks ∗ Jeffrey M. Walls University of Michigan Ann Arbor, Michigan jmwalls...acoustic cooperative localization. Our system leverages communica- tion within an acoustic network to both share navigation information and measure the...three vehicle cooperative network and provide a performance summary over several field trials. Categories and Subject Descriptors I.2.9 [Robotics

  14. Distributed Prognostics and Health Management with a Wireless Network Architecture

    NASA Technical Reports Server (NTRS)

    Goebel, Kai; Saha, Sankalita; Sha, Bhaskar

    2013-01-01

    A heterogeneous set of system components monitored by a varied suite of sensors and a particle-filtering (PF) framework, with the power and the flexibility to adapt to the different diagnostic and prognostic needs, has been developed. Both the diagnostic and prognostic tasks are formulated as a particle-filtering problem in order to explicitly represent and manage uncertainties in state estimation and remaining life estimation. Current state-of-the-art prognostic health management (PHM) systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to a loss in functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become for a number of reasons somewhat ungainly for successful deployment, and efficient distributed architectures can be more beneficial. The distributed health management architecture is comprised of a network of smart sensor devices. These devices monitor the health of various subsystems or modules. They perform diagnostics operations and trigger prognostics operations based on user-defined thresholds and rules. The sensor devices, called computing elements (CEs), consist of a sensor, or set of sensors, and a communication device (i.e., a wireless transceiver beside an embedded processing element). The CE runs in either a diagnostic or prognostic operating mode. The diagnostic mode is the default mode where a CE monitors a given subsystem or component through a low-weight diagnostic algorithm. If a CE detects a critical condition during monitoring, it raises a flag. Depending on availability of resources, a networked local cluster of CEs is formed that then carries out prognostics and fault mitigation by efficient distribution of the tasks. It should be noted that the CEs are expected not to suspend their previous tasks in the prognostic mode. When the

  15. Pruning Neural Networks with Distribution Estimation Algorithms

    SciTech Connect

    Cantu-Paz, E

    2003-01-15

    This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algorithm (GA), the paper considers three distribution estimation algorithms (DEAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the DEAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a feed forward neural network trained with standard back propagation and public-domain and artificial data sets. The pruned networks seemed to have better or equal accuracy than the original fully-connected networks. Only in a few cases, pruning resulted in less accurate networks. We found few differences in the accuracy of the networks pruned by the four EAs, but found important differences in the execution time. The results suggest that a simple GA with a small population might be the best algorithm for pruning networks on the data sets we tested.

  16. Neural Network Algorithm for Particle Loading

    SciTech Connect

    J. L. V. Lewandowski

    2003-04-25

    An artificial neural network algorithm for continuous minimization is developed and applied to the case of numerical particle loading. It is shown that higher-order moments of the probability distribution function can be efficiently renormalized using this technique. A general neural network for the renormalization of an arbitrary number of moments is given.

  17. ROADM architectures and technologies for agile optical networks

    NASA Astrophysics Data System (ADS)

    Eldada, Louay A.

    2007-02-01

    We review the different optoelectronic component and module technologies that have been developed for use in ROADM subsystems, and describe their principles of operation, designs, features, advantages, and challenges. We also describe the various needs for reconfigurable optical add/drop switching in agile optical networks. For each network need, we present the different ROADM subsystem architecture options with their pros and cons, and describe the optoelectronic technologies supporting each architecture.

  18. Genetic algorithm for neural networks optimization

    NASA Astrophysics Data System (ADS)

    Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

    2004-11-01

    This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

  19. A Parallel Attractor Finding Algorithm Based on Boolean Satisfiability for Genetic Regulatory Networks

    PubMed Central

    Guo, Wensheng; Yang, Guowu; Wu, Wei; He, Lei; Sun, Mingyu

    2014-01-01

    In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures. PMID:24718686

  20. A parallel attractor-finding algorithm based on Boolean satisfiability for genetic regulatory networks.

    PubMed

    Guo, Wensheng; Yang, Guowu; Wu, Wei; He, Lei; Sun, Mingyu

    2014-01-01

    In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures.

  1. Using overlay network architectures for scalable video distribution

    NASA Astrophysics Data System (ADS)

    Patrikakis, Charalampos Z.; Despotopoulos, Yannis; Fafali, Paraskevi; Cha, Jihun; Kim, Kyuheon

    2004-11-01

    Within the last years, the enormous growth of Internet based communication as well as the rapid increase of available processing power has lead to the widespread use of multimedia streaming as a means to convey information. This work aims at providing an open architecture designed to support scalable streaming to a large number of clients using application layer multicast. The architecture is based on media relay nodes that can be deployed transparently to any existing media distribution scheme, which can support media streamed using the RTP and RTSP protocols. The architecture is based on overlay networks at application level, featuring rate adaptation mechanisms for responding to network congestion.

  2. Efficient training algorithms for a class of shunting inhibitory convolutional neural networks.

    PubMed

    Tivive, Fok Hing Chi; Bouzerdoum, Abdesselam

    2005-05-01

    This article presents some efficient training algorithms, based on first-order, second-order, and conjugate gradient optimization methods, for a class of convolutional neural networks (CoNNs), known as shunting inhibitory convolution neural networks. Furthermore, a new hybrid method is proposed, which is derived from the principles of Quickprop, Rprop, SuperSAB, and least squares (LS). Experimental results show that the new hybrid method can perform as well as the Levenberg-Marquardt (LM) algorithm, but at a much lower computational cost and less memory storage. For comparison sake, the visual pattern recognition task of face/nonface discrimination is chosen as a classification problem to evaluate the performance of the training algorithms. Sixteen training algorithms are implemented for the three different variants of the proposed CoNN architecture: binary-, Toeplitz- and fully connected architectures. All implemented algorithms can train the three network architectures successfully, but their convergence speed vary markedly. In particular, the combination of LS with the new hybrid method and LS with the LM method achieve the best convergence rates in terms of number of training epochs. In addition, the classification accuracies of all three architectures are assessed using ten-fold cross validation. The results show that the binary- and Toeplitz-connected architectures outperform slightly the fully connected architecture: the lowest error rates across all training algorithms are 1.95% for Toeplitz-connected, 2.10% for the binary-connected, and 2.20% for the fully connected network. In general, the modified Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods, the three variants of LM algorithm, and the new hybrid/LS method perform consistently well, achieving error rates of less than 3% averaged across all three architectures.

  3. NASA Integrated Network Monitor and Control Software Architecture

    NASA Technical Reports Server (NTRS)

    Shames, Peter; Anderson, Michael; Kowal, Steve; Levesque, Michael; Sindiy, Oleg; Donahue, Kenneth; Barnes, Patrick

    2012-01-01

    The National Aeronautics and Space Administration (NASA) Space Communications and Navigation office (SCaN) has commissioned a series of trade studies to define a new architecture intended to integrate the three existing networks that it operates, the Deep Space Network (DSN), Space Network (SN), and Near Earth Network (NEN), into one integrated network that offers users a set of common, standardized, services and interfaces. The integrated monitor and control architecture utilizes common software and common operator interfaces that can be deployed at all three network elements. This software uses state-of-the-art concepts such as a pool of re-programmable equipment that acts like a configurable software radio, distributed hierarchical control, and centralized management of the whole SCaN integrated network. For this trade space study a model-based approach using SysML was adopted to describe and analyze several possible options for the integrated network monitor and control architecture. This model was used to refine the design and to drive the costing of the four different software options. This trade study modeled the three existing self standing network elements at point of departure, and then described how to integrate them using variations of new and existing monitor and control system components for the different proposed deployments under consideration. This paper will describe the trade space explored, the selected system architecture, the modeling and trade study methods, and some observations on useful approaches to implementing such model based trade space representation and analysis.

  4. Clinical Decision Support Systems for Comorbidity: Architecture, Algorithms, and Applications

    PubMed Central

    Fan, Aihua; Tang, Yu

    2017-01-01

    In this paper, we present the design of a clinical decision support system (CDSS) for monitoring comorbid conditions. Specifically, we address the architecture of a CDSS by characterizing it from three layers and discuss the algorithms in each layer. Also we address the applications of CDSSs in a few real scenarios and analyze the accuracy of a CDSS in consideration of the potential conflicts when using multiple clinical practice guidelines concurrently. Finally, we compare the system performance in our design with that in the other design schemes. Our study shows that our proposed design can achieve a clinical decision in a shorter time than the other designs, while ensuring a high level of system accuracy. PMID:28373881

  5. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms.

    PubMed

    Garro, Beatriz A; Vázquez, Roberto A

    2015-01-01

    Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems.

  6. Architecture for Cognitive Networking within NASAs Future Space Communications Infrastructure

    NASA Technical Reports Server (NTRS)

    Clark, Gilbert J., III; Eddy, Wesley M.; Johnson, Sandra K.; Barnes, James; Brooks, David

    2016-01-01

    Future space mission concepts and designs pose many networking challenges for command, telemetry, and science data applications with diverse end-to-end data delivery needs. For future end-to-end architecture designs, a key challenge is meeting expected application quality of service requirements for multiple simultaneous mission data flows with options to use diverse onboard local data buses, commercial ground networks, and multiple satellite relay constellations in LEO, MEO, GEO, or even deep space relay links. Effectively utilizing a complex network topology requires orchestration and direction that spans the many discrete, individually addressable computer systems, which cause them to act in concert to achieve the overall network goals. The system must be intelligent enough to not only function under nominal conditions, but also adapt to unexpected situations, and reorganize or adapt to perform roles not originally intended for the system or explicitly programmed. This paper describes architecture features of cognitive networking within the future NASA space communications infrastructure, and interacting with the legacy systems and infrastructure in the meantime. The paper begins by discussing the need for increased automation, including inter-system collaboration. This discussion motivates the features of an architecture including cognitive networking for future missions and relays, interoperating with both existing endpoint-based networking models and emerging information-centric models. From this basis, we discuss progress on a proof-of-concept implementation of this architecture as a cognitive networking on-orbit application on the SCaN Testbed attached to the International Space Station.

  7. A Novel Energy-Driven Architecture for Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Jayakody, D. N. K.; Khan, Z. A.; Rodrigo; de Lamare, C.; Thompson, J.

    2017-01-01

    This paper proposes a novel Energy-Driven Architecture (EDA) as a durable architecture and considers almost all principal energy constituents of wireless sensor networks applications. By creating a single overall model, a tolerable formulation is then offered to communicate the total energy use of a wireless sensor network application regarding the energy constituents. The formulation provides a tangible illustration for analyzing the performance of a wireless sensor network application, optimizing its constituent’s operations, as well as creating more energy saving applications. The simulations are employed to show the feasibility of our model and also energy formulation.

  8. On-board processing satellite network architectures for broadband ISDN

    NASA Technical Reports Server (NTRS)

    Inukai, Thomas; Faris, Faris; Shyy, Dong-Jye

    1992-01-01

    Onboard baseband processing architectures for future satellite broadband integrated services digital networks (B-ISDN's) are addressed. To assess the feasibility of implementing satellite B-ISDN services, critical design issues, such as B-ISDN traffic characteristics, transmission link design, and a trade-off between onboard circuit and fast packet switching, are analyzed. Examples of the two types of switching mechanisms and potential onboard network control functions are presented. A sample network architecture is also included to illustrate a potential onboard processing system.

  9. Network Games and Approximation Algorithms

    DTIC Science & Technology

    2008-01-03

    I also spend time during the last three years writing a textbook on Algorithm Design (with Jon Kleinberg) that had now been adopted by a number of...Minimum-Size Bounded-Capacity Cut (MSBCC) problem, in which we are given a graph with an identified source and seek to find a cut minimizing the number ...Distributed Computing (Special Issue PODC 05) Volume 19, Number 4, 2007, 255-266. http://www.springerlink.com/content/x 148746507861 np7/ ?p

  10. Image-restoration algorithms for a fully connected architecture.

    PubMed

    Abbiss, J B; Brames, B J; Byrne, C L; Fiddy, M A

    1990-06-15

    We describe the implementation of a technique for achieving image superresolution using a fully connected network of simple processors operating in an iterative mode. We show that an updating scheme can be specified that ensures convergence for the serial (asynchronous) updating case. With the appropriate hardware, parallel (synchronous) updating becomes of particular interest because of the potential for accelerated convergence; it is this approach that we envisage implementing in optical hardware. For this case also, we present a convergent scheme that can be related to a regularized form of the Gerchberg-Papoulis algorithm.

  11. An architecture for the efficient implementation of compressive sampling reconstruction algorithms in reconfigurable hardware

    NASA Astrophysics Data System (ADS)

    Ortiz, Fernando E.; Kelmelis, Eric J.; Arce, Gonzalo R.

    2007-04-01

    According to the Shannon-Nyquist theory, the number of samples required to reconstruct a signal is proportional to its bandwidth. Recently, it has been shown that acceptable reconstructions are possible from a reduced number of random samples, a process known as compressive sampling. Taking advantage of this realization has radical impact on power consumption and communication bandwidth, crucial in applications based on small/mobile/unattended platforms such as UAVs and distributed sensor networks. Although the benefits of these compression techniques are self-evident, the reconstruction process requires the solution of nonlinear signal processing algorithms, which limit applicability in portable and real-time systems. In particular, (1) the power consumption associated with the difficult computations offsets the power savings afforded by compressive sampling, and (2) limited computational power prevents these algorithms to maintain pace with the data-capturing sensors, resulting in undesirable data loss. FPGA based computers offer low power consumption and high computational capacity, providing a solution to both problems simultaneously. In this paper, we present an architecture that implements the algorithms central to compressive sampling in an FPGA environment. We start by studying the computational profile of the convex optimization algorithms used in compressive sampling. Then we present the design of a pixel pipeline suitable for FPGA implementation, able to compute these algorithms.

  12. Transcriptional Network Architecture of Breast Cancer Molecular Subtypes.

    PubMed

    de Anda-Jáuregui, Guillermo; Velázquez-Caldelas, Tadeo E; Espinal-Enríquez, Jesús; Hernández-Lemus, Enrique

    2016-01-01

    Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.

  13. The architecture of a network level intrusion detection system

    SciTech Connect

    Heady, R.; Luger, G.; Maccabe, A.; Servilla, M.

    1990-08-15

    This paper presents the preliminary architecture of a network level intrusion detection system. The proposed system will monitor base level information in network packets (source, destination, packet size, and time), learning the normal patterns and announcing anomalies as they occur. The goal of this research is to determine the applicability of current intrusion detection technology to the detection of network level intrusions. In particular, the authors are investigating the possibility of using this technology to detect and react to worm programs.

  14. Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

    PubMed Central

    de Anda-Jáuregui, Guillermo; Velázquez-Caldelas, Tadeo E.; Espinal-Enríquez, Jesús; Hernández-Lemus, Enrique

    2016-01-01

    Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer. PMID:27920729

  15. Algorithms for security in robotics and networks

    NASA Astrophysics Data System (ADS)

    Simov, Borislav Hristov

    The dissertation presents algorithms for robotics and security. The first chapter gives an overview of the area of visibility-based pursuit-evasion. The following two chapters introduce two specific algorithms in that area. The algorithms are based on research done together with Dr. Giora Slutzki and Dr. Steven LaValle. Chapter 2 presents a polynomial-time algorithm for clearing a polygon by a single 1-searcher. The result is extended to a polynomial-time algorithm for a pair of 1-searchers in Chapter 3. Chapters 4 and 5 contain joint research with Dr. Srini Tridandapani, Dr. Jason Jue and Dr. Michael Borella in the area of computer networks. Chapter 4 presents a method of providing privacy over an insecure channel which does not require encryption. Chapter 5 gives approximate bounds for the link utilization in multicast traffic.

  16. Unstructured Peer-to-Peer Network Architectures

    NASA Astrophysics Data System (ADS)

    Jin, Xing; Chan, S.-H. Gary

    With the rapid growth of the Internet, peer-to-peer P2P networks have been widely studied and deployed. According to CacheLogic Research, P2P traffic has dominated the Internet traffic in 2006, by accounting for over 72% Internet traffic. In this chapter, we focus on unstructured P2P networks, one key type of P2P networks. We first present several unstructured P2P networks for the file sharing application, and then investigate some advanced issues in the network design. We also study two other important applications, i.e., media streaming and voice over Internet Protocol (VoIP). Finally, we discuss unstructured P2P networks over wireless networks.

  17. Efficient VLSI architecture for training radial basis function networks.

    PubMed

    Fan, Zhe-Cheng; Hwang, Wen-Jyi

    2013-03-19

    This paper presents a novel VLSI architecture for the training of radial basis function (RBF) networks. The architecture contains the circuits for fuzzy C-means (FCM) and the recursive Least Mean Square (LMS) operations. The FCM circuit is designed for the training of centers in the hidden layer of the RBF network. The recursive LMS circuit is adopted for the training of connecting weights in the output layer. The architecture is implemented by the field programmable gate array (FPGA). It is used as a hardware accelerator in a system on programmable chip (SOPC) for real-time training and classification. Experimental results reveal that the proposed RBF architecture is an effective alternative for applications where fast and efficient RBF training is desired.

  18. Multi-layered greedy network-growing algorithm: extension of greedy network-growing algorithm to multi-layered networks.

    PubMed

    Kamimura, Ryotaro

    2004-02-01

    In this paper, we extend our greedy network-growing algorithm to multi-layered networks. With multi-layered networks, we can solve many complex problems that single-layered networks fail to solve. In addition, the network-growing algorithm is used in conjunction with teacher-directed learning that produces appropriate outputs without computing errors between targets and outputs. Thus, the present algorithm is a very efficient network-growing algorithm. The new algorithm was applied to three problems: the famous vertical-horizontal lines detection problem, a medical data problem and a road classification problem. In all these cases, experimental results confirmed that the method could solve problems that single-layered networks failed to. In addition, information maximization makes it possible to extract salient features in input patterns.

  19. Operational Concepts for a Generic Space Exploration Communication Network Architecture

    NASA Technical Reports Server (NTRS)

    Ivancic, William D.; Vaden, Karl R.; Jones, Robert E.; Roberts, Anthony M.

    2015-01-01

    This document is one of three. It describes the Operational Concept (OpsCon) for a generic space exploration communication architecture. The purpose of this particular document is to identify communication flows and data types. Two other documents accompany this document, a security policy profile and a communication architecture document. The operational concepts should be read first followed by the security policy profile and then the architecture document. The overall goal is to design a generic space exploration communication network architecture that is affordable, deployable, maintainable, securable, evolvable, reliable, and adaptable. The architecture should also require limited reconfiguration throughout system development and deployment. System deployment includes: subsystem development in a factory setting, system integration in a laboratory setting, launch preparation, launch, and deployment and operation in space.

  20. Security Policy for a Generic Space Exploration Communication Network Architecture

    NASA Technical Reports Server (NTRS)

    Ivancic, William D.; Sheehe, Charles J.; Vaden, Karl R.

    2016-01-01

    This document is one of three. It describes various security mechanisms and a security policy profile for a generic space-based communication architecture. Two other documents accompany this document- an Operations Concept (OpsCon) and a communication architecture document. The OpsCon should be read first followed by the security policy profile described by this document and then the architecture document. The overall goal is to design a generic space exploration communication network architecture that is affordable, deployable, maintainable, securable, evolvable, reliable, and adaptable. The architecture should also require limited reconfiguration throughout system development and deployment. System deployment includes subsystem development in a factory setting, system integration in a laboratory setting, launch preparation, launch, and deployment and operation in space.

  1. Evaluating the Multicast Control Protocol on a Multicasting Network Architecture

    NASA Astrophysics Data System (ADS)

    Stergiou, E.; Meletiou, G.; Vasiliadis, D. C.; Rizos, G. E.; Margariti, S. V.

    2007-12-01

    In this paper a reliable multicasting architecture presented. This architecture operates using a Multicasting Firewall over the Multicast Control Protocol (MCP). Our aim was to evaluate the transition times of specific packets such as the IGMPv2 reports in the proposed multicasting network. In our study, multicasting experiments presented and analyzed. The obtained results of our experiments clearly show that the average validation times of IGMP reports on the Multicast Control Server smoothly increase versus the number of clients.

  2. An OSI architecture for the Deep Space Network

    NASA Technical Reports Server (NTRS)

    Heuser, W. R.

    1992-01-01

    This article presents an Open Systems Interconnection (OSI) architecture developed for the Deep Space Network. An historical review is provided to establish the context for current United States Government policy on interprocessor communication standards. An introduction to the OSI architecture, its seven-layer approach, and an overview of application service entities are furnished as a tutorial. Finally, the results of a prototype system developed for monitor and control of a Deep Space Station are also presented.

  3. Architectural and Markovian factors of echo state networks.

    PubMed

    Gallicchio, Claudio; Micheli, Alessio

    2011-06-01

    Echo State Networks (ESNs) constitute an emerging approach for efficiently modeling Recurrent Neural Networks (RNNs). In this paper we investigate some of the main aspects that can be accounted for the success and limitations of this class of models. In particular, we propose complementary classes of factors related to contractivity and architecture of reservoirs and we study their relative relevance. First, we show the existence of a class of tasks for which ESN performance is independent of the architectural design. The effect of the Markovian factor, characterizing a significant class within these cases, is shown by introducing instances of easy/hard tasks for ESNs featured by contractivity of reservoir dynamics. In the complementary cases, for which architectural design is effective, we investigate and decompose the aspects of network design that allow a larger reservoir to progressively improve the predictive performance. In particular, we introduce four key architectural factors: input variability, multiple time-scales dynamics, non-linear interactions among units and regression in an augmented feature space. To investigate the quantitative effects of the different architectural factors within this class of tasks successfully approached by ESNs, variants of the basic ESN model are proposed and tested on instances of datasets of different nature and difficulty. Experimental evidences confirm the role of the Markovian factor and show that all the identified key architectural factors have a major role in determining ESN performances.

  4. Enterprise Management Network Architecture: The Organization Layer

    DTIC Science & Technology

    1990-11-01

    Distributed Systems capabilities 7 1.25 Distributed Systems Problems 7 2. Enterprise Management Network Node 8 3. Organization Layer 12 3.1 Modeling ...hierarchies, authority relations, etc., and the modeling of technologies being used such as network and database types. " The realizational or physical level...section, we define the content and use of the tools used to acquire the description of the organizational model of the Enterprise Management Network (EMU

  5. A spiking neural network architecture for nonlinear function approximation.

    PubMed

    Iannella, N; Back, A D

    2001-01-01

    Multilayer perceptrons have received much attention in recent years due to their universal approximation capabilities. Normally, such models use real valued continuous signals, although they are loosely based on biological neuronal networks that encode signals using spike trains. Spiking neural networks are of interest both from a biological point of view and in terms of a method of robust signaling in particularly noisy or difficult environments. It is important to consider networks based on spike trains. A basic question that needs to be considered however, is what type of architecture can be used to provide universal function approximation capabilities in spiking networks? In this paper, we propose a spiking neural network architecture using both integrate-and-fire units as well as delays, that is capable of approximating a real valued function mapping to within a specified degree of accuracy.

  6. Convolutional neural network architectures for predicting DNA–protein binding

    PubMed Central

    Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.

    2016-01-01

    Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608

  7. On-board processing satellite network architecture and control study

    NASA Technical Reports Server (NTRS)

    Campanella, S. Joseph; Pontano, Benjamin A.; Chalmers, Harvey

    1987-01-01

    The market for telecommunications services needs to be segmented into user classes having similar transmission requirements and hence similar network architectures. Use of the following transmission architecture was considered: satellite switched TDMA; TDMA up, TDM down; scanning (hopping) beam TDMA; FDMA up, TDM down; satellite switched MF/TDMA; and switching Hub earth stations with double hop transmission. A candidate network architecture will be selected that: comprises multiple access subnetworks optimized for each user; interconnects the subnetworks by means of a baseband processor; and optimizes the marriage of interconnection and access techniques. An overall network control architecture will be provided that will serve the needs of the baseband and satellite switched RF interconnected subnetworks. The results of the studies shall be used to identify elements of network architecture and control that require the greatest degree of technology development to realize an operational system. This will be specified in terms of: requirements of the enabling technology; difference from the current available technology; and estimate of the development requirements needed to achieve an operational system. The results obtained for each of these tasks are presented.

  8. Design and architecture of the Mars relay network planning and analysis framework

    NASA Technical Reports Server (NTRS)

    Cheung, K. M.; Lee, C. H.

    2002-01-01

    In this paper we describe the design and architecture of the Mars Network planning and analysis framework that supports generation and validation of efficient planning and scheduling strategy. The goals are to minimize the transmitting time, minimize the delaying time, and/or maximize the network throughputs. The proposed framework would require (1) a client-server architecture to support interactive, batch, WEB, and distributed analysis and planning applications for the relay network analysis scheme, (2) a high-fidelity modeling and simulation environment that expresses link capabilities between spacecraft to spacecraft and spacecraft to Earth stations as time-varying resources, and spacecraft activities, link priority, Solar System dynamic events, the laws of orbital mechanics, and other limiting factors as spacecraft power and thermal constraints, (3) an optimization methodology that casts the resource and constraint models into a standard linear and nonlinear constrained optimization problem that lends itself to commercial off-the-shelf (COTS)planning and scheduling algorithms.

  9. Neural network based architectures for aerospace applications

    NASA Technical Reports Server (NTRS)

    Ricart, Richard

    1987-01-01

    A brief history of the field of neural networks research is given and some simple concepts are described. In addition, some neural network based avionics research and development programs are reviewed. The need for the United States Air Force and NASA to assume a leadership role in supporting this technology is stressed.

  10. Parallel Architectures and Parallel Algorithms for Integrated Vision Systems. Ph.D. Thesis

    NASA Technical Reports Server (NTRS)

    Choudhary, Alok Nidhi

    1989-01-01

    Computer vision is regarded as one of the most complex and computationally intensive problems. An integrated vision system (IVS) is a system that uses vision algorithms from all levels of processing to perform for a high level application (e.g., object recognition). An IVS normally involves algorithms from low level, intermediate level, and high level vision. Designing parallel architectures for vision systems is of tremendous interest to researchers. Several issues are addressed in parallel architectures and parallel algorithms for integrated vision systems.

  11. Power, Avionics and Software Communication Network Architecture

    NASA Technical Reports Server (NTRS)

    Ivancic, William D.; Sands, Obed S.; Bakula, Casey J.; Oldham, Daniel R.; Wright, Ted; Bradish, Martin A.; Klebau, Joseph M.

    2014-01-01

    This document describes the communication architecture for the Power, Avionics and Software (PAS) 2.0 subsystem for the Advanced Extravehicular Mobile Unit (AEMU). The following systems are described in detail: Caution Warn- ing and Control System, Informatics, Storage, Video, Audio, Communication, and Monitoring Test and Validation. This document also provides some background as well as the purpose and goals of the PAS project at Glenn Research Center (GRC).

  12. Underwater sensor networks: a new energy efficient and robust architecture.

    PubMed

    Climent, Salvador; Capella, Juan Vicente; Meratnia, Nirvana; Serrano, Juan José

    2012-01-01

    The specific characteristics of underwater environments introduce new challenges for networking protocols. In this paper, a specialized architecture for underwater sensor networks (UWSNs) is proposed and evaluated. Experiments are conducted in order to analyze the suitability of this protocol for the subaquatic transmission medium. Moreover, different scheduling techniques are applied to the architecture in order to study their performance. In addition, given the harsh conditions of the underwater medium, different retransmission methods are combined with the scheduling techniques. Finally, simulation results illustrate the performance achievements of the proposed protocol in end-to-end delay, packet delivery ratio and energy consumption, showing that this protocol can be very suitable for the underwater medium.

  13. Low Power S-Box Architecture for AES Algorithm using Programmable Second Order Reversible Cellular Automata: An Application to WBAN.

    PubMed

    Gangadari, Bhoopal Rao; Ahamed, Shaik Rafi

    2016-12-01

    In this paper, we presented a novel approach of low energy consumption architecture of S-Box used in Advanced Encryption Standard (AES) algorithm using programmable second order reversible cellular automata (RCA (2)). The architecture entails a low power implementation with minimal delay overhead and the performance of proposed RCA (2) based S-Box in terms of security is evaluated using the cryptographic properties such as nonlinearity, correlation immunity bias, strict avalanche criteria, entropy and also found that the proposed architecture is secure enough for cryptographic applications. Moreover, the proposed AES algorithm architecture simulation studies show that energy consumption of 68.726 nJ, power dissipation of 3.856 mW for 0.18- μm at 13.69 MHz and energy consumption of 29.408 nJ, power dissipation of 1.65 mW for 0.13- μm at 13.69 MHz. The proposed AES algorithm with RCA (2) based S-Box shows a reduction power consumption by 50 % and energy consumption by 5 % compared to best classical S-Box and composite field arithmetic based AES algorithm. Apart from that, it is also shown that RCA (2) based S-Boxes are dynamic in nature, invertible, low power dissipation compared to that of LUT based S-Box and hence suitable for Wireless Body Area Network (WBAN) applications.

  14. Towards blueprints for network architecture, biophysical dynamics and signal transduction.

    PubMed

    Coombes, Stephen; Doiron, Brent; Josić, Kresimir; Shea-Brown, Eric

    2006-12-15

    We review mathematical aspects of biophysical dynamics, signal transduction and network architecture that have been used to uncover functionally significant relations between the dynamics of single neurons and the networks they compose. We focus on examples that combine insights from these three areas to expand our understanding of systems neuroscience. These range from single neuron coding to models of decision making and electrosensory discrimination by networks and populations and also coincidence detection in pairs of dendrites and dynamics of large networks of excitable dendritic spines. We conclude by describing some of the challenges that lie ahead as the applied mathematics community seeks to provide the tools which will ultimately underpin systems neuroscience.

  15. A model selection algorithm for a posteriori probability estimation with neural networks.

    PubMed

    Arribas, Juan Ignacio; Cid-Sueiro, Jesús

    2005-07-01

    This paper proposes a novel algorithm to jointly determine the structure and the parameters of a posteriori probability model based on neural networks (NNs). It makes use of well-known ideas of pruning, splitting, and merging neural components and takes advantage of the probabilistic interpretation of these components. The algorithm, so called a posteriori probability model selection (PPMS), is applied to an NN architecture called the generalized softmax perceptron (GSP) whose outputs can be understood as probabilities although results shown can be extended to more general network architectures. Learning rules are derived from the application of the expectation-maximization algorithm to the GSP-PPMS structure. Simulation results show the advantages of the proposed algorithm with respect to other schemes.

  16. Impact of network sharing in multi-core architectures.

    SciTech Connect

    Narayanaswamy, G.; Balaji, P.; Feng, W.; Mathematics and Computer Science; Virginia Tech

    2008-01-01

    As commodity components continue to dominate the realm of high-end computing, two hardware trends have emerged as major contributors-high-speed networking technologies and multi-core architectures. Communication middleware such as the Message Passing Interface (MPI) uses the network technology for communicating between processes that reside on different physical nodes, while using shared memory for communicating between processes on different cores within the same node. Thus, two conflicting possibilities arise: (i) with the advent of multi-core architectures, the number of processes that reside on the same physical node and hence share the same physical network can potentially increase significantly, resulting in increased network usage, and (ii) given the increase in intra-node shared-memory communication for processes residing on the same node, the network usage can potentially decrease significantly. In this paper, we address these two conflicting possibilities and study the behavior of network usage in multi-core environments with sample scientific applications. Specifically, we analyze trends that result in increase or decrease of network usage, and we derive insights into application performance based on these. We also study the sharing of different resources in the system in multi-core environments and identify the contribution of the network in this mix. In addition, we study different process allocation strategies and analyze their impact on such network sharing.

  17. An OSI architecture for the deep space network

    NASA Technical Reports Server (NTRS)

    Heuser, W. Randy; Cooper, Lynne P.

    1993-01-01

    The flexibility and robustness of a monitor and control system are a direct result of the underlying inter-processor communications architecture. A new architecture for monitor & Control at the Deep Space Network Communications Complexes has been developed based on the Open System Interconnection (OSI) standards. The suitability of OSI standards for DSN M&C has been proven in the laboratory. The laboratory success has resulted in choosing an OSI-based architecture for DSS-13 M&C. DSS-13 is the DSN experimental station and is not part of the 'operational' DSN; it's role is to provide an environment to test new communications concepts can be tested and conduct unique science experiments. Therefore, DSS-13 must be robust enough to support operational activities, while also being flexible enough to enable experimentation. This paper describes the M&C architecture developed for DSS-13 and the results from system and operational testing.

  18. Mesoscale architecture shapes initiation and richness of spontaneous network activity.

    PubMed

    Okujeni, Samora; Kandler, Steffen; Egert, Ulrich

    2017-03-14

    Spontaneous activity in the absence of external input, including propagating waves of activity, is a robust feature of neuronal networks in vivo and in vitro. The neurophysiological and anatomical requirements for initiation and persistence of such activity, however, are poorly understood, as is their role in the function of neuronal networks. Computational network studies indicate that clustered connectivity may foster the generation, maintenance and richness of spontaneous activity. Since this mesoscale architecture cannot be systematically modified in intact tissue, testing these predictions is impracticable in vivo. Here, we investigate how the mesoscale structure shapes spontaneous activity in generic networks of rat cortical neurons in vitro. In these networks, neurons spontaneously arrange into local clusters with high neurite density and form fasciculating long-range axons. We modified this structure by modulation of protein kinase C, an enzyme regulating neurite growth and cell migration. Inhibition of protein kinase C reduced neuronal aggregation and fasciculation of axons, i.e. promoted uniform architecture. Conversely, activation of protein kinase C promoted aggregation of neurons into clusters, local connectivity and bundling of long-range axons. Supporting predictions from theory, clustered networks were more spontaneously active and generated diverse activity patterns. Neurons within clusters received stronger synaptic inputs and displayed increased membrane potential fluctuations. Intensified clustering promoted the initiation of synchronous bursting events but entailed incomplete network recruitment. Moderately clustered networks appear optimal for initiation and propagation of diverse patterns of activity. Our findings support a crucial role of the mesoscale architectures in the regulation of spontaneous activity dynamics.SIGNIFICANCE STATEMENTComputational studies predict richer and persisting spatio-temporal patterns of spontaneous activity in

  19. Integrated network architecture for sustained human and robotic exploration

    NASA Technical Reports Server (NTRS)

    Noreen, Gary K.; Cesarone, Robert; Deutsch, Leslie; Edwards, Charlie; Soloff, Jason; Ely, Todd; Cook, Brian; Morabito, David; Hemmati, Hamid; Piazzolla, Sabino; Hastrup, Rolf; Abraham, Douglas

    2005-01-01

    The National Aeronautics and Space Administration (NASA) Exploration Systems Mission Directorate is planning a series of human and robotic missions to the Earth's moon and to Mars. These missions will require telecommunication and navigation services. This paper sets forth presumed requirements for such services and presents strawman lunar and Mars telecommunications network architectures to satisfy the presumed requirements.

  20. Adaptive Routing Algorithm for Priority Flows in a Network

    DTIC Science & Technology

    2012-06-14

    ADAPTIVE ROUTING ALGORITHM FOR PRIORITY FLOWS IN A NETWORK THESIS Timothy J. Carbino, Captain...ADAPTIVE ROUTING ALGORITHM FOR PRIORITY FLOWS IN A NETWORK THESIS Presented to the Faculty Department of Electrical and Computer... Thesis 20 Aug 10 – 14 Jun 12 Adaptive Routing Algorithm for Priority Flows in a Network 12629PCarbino, Timothy J, Captain, USAF Air Force Institute of

  1. Algorithms and architectures for high performance analysis of semantic graphs.

    SciTech Connect

    Hendrickson, Bruce Alan

    2005-09-01

    analysis. Since intelligence datasets can be extremely large, the focus of this work is on the use of parallel computers. We have been working to develop scalable parallel algorithms that will be at the core of a semantic graph analysis infrastructure. Our work has involved two different thrusts, corresponding to two different computer architectures. The first architecture of interest is distributed memory, message passing computers. These machines are ubiquitous and affordable, but they are challenging targets for graph algorithms. Much of our distributed-memory work to date has been collaborative with researchers at Lawrence Livermore National Laboratory and has focused on finding short paths on distributed memory parallel machines. Our implementation on 32K processors of BlueGene/Light finds shortest paths between two specified vertices in just over a second for random graphs with 4 billion vertices.

  2. A hybrid layer-multiplexing and pipeline architecture for efficient FPGA-based multilayer neural network

    NASA Astrophysics Data System (ADS)

    Dong, Yiping; Li, Ce; Lin, Zhen; Watanabe, Takahiro

    This paper presents a novel architecture for an FPGA-based implementation of multilayer Artificial Neural Network (ANN), which integrates both the layer-multiplexing and pipeline architecture. Given a kind of FPGA to be used, the proposed method aims at enhancing the efficiency of resource usage of the FPGA and improving the forward speed at the module level, so that a larger ANN can be implemented on traditional FPGAs and also a high performance is achieved. Usually FPGA board is not changed for every applications, thus, we need not mind about the usage of it if the application can be implemented within the resource limitation. We developed a new mapping method from ANN schematic to FPGA by using this hybrid architecture, and also developed an algorithm to automatically determine the architecture by optimizing the application specific neural network topology. The experimental results show that the proposed architecture can produce a very compact circuit for multilayer ANN to meet resource limitation of a given FPGA. Furthermore, higher performance is obtained as compared with conventional methods.

  3. Robust Networking Architecture and Secure Communication Scheme for Heterogeneous Wireless Sensor Networks

    ERIC Educational Resources Information Center

    McNeal, McKenzie, III.

    2012-01-01

    Current networking architectures and communication protocols used for Wireless Sensor Networks (WSNs) have been designed to be energy efficient, low latency, and long network lifetime. One major issue that must be addressed is the security in data communication. Due to the limited capabilities of low cost and small sized sensor nodes, designing…

  4. Network-based recommendation algorithms: A review

    NASA Astrophysics Data System (ADS)

    Yu, Fei; Zeng, An; Gillard, Sébastien; Medo, Matúš

    2016-06-01

    Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use-such as the possible influence of recommendation on the evolution of systems that use it-and finally discuss open research directions and challenges.

  5. Neural Network-Based Hyperspectral Algorithms

    DTIC Science & Technology

    2016-06-07

    our effort is development of robust numerical inversion algorithms, which will retrieve inherent optical properties of the water column as well as...combination of in-situ and model data of water column variables (IOP’s, depth, bottom type, upwelling radiance, etc.) a neural network non-linear...function approximation model will be used to establish the inverse relationship between upwelling surface radiance and the water column variables, 2

  6. Building and measuring a high performance network architecture

    SciTech Connect

    Kramer, William T.C.; Toole, Timothy; Fisher, Chuck; Dugan, Jon; Wheeler, David; Wing, William R; Nickless, William; Goddard, Gregory; Corbato, Steven; Love, E. Paul; Daspit, Paul; Edwards, Hal; Mercer, Linden; Koester, David; Decina, Basil; Dart, Eli; Paul Reisinger, Paul; Kurihara, Riki; Zekauskas, Matthew J; Plesset, Eric; Wulf, Julie; Luce, Douglas; Rogers, James; Duncan, Rex; Mauth, Jeffery

    2001-04-20

    Once a year, the SC conferences present a unique opportunity to create and build one of the most complex and highest performance networks in the world. At SC2000, large-scale and complex local and wide area networking connections were demonstrated, including large-scale distributed applications running on different architectures. This project was designed to use the unique opportunity presented at SC2000 to create a testbed network environment and then use that network to demonstrate and evaluate high performance computational and communication applications. This testbed was designed to incorporate many interoperable systems and services and was designed for measurement from the very beginning. The end results were key insights into how to use novel, high performance networking technologies and to accumulate measurements that will give insights into the networks of the future.

  7. An introduction to bio-inspired artificial neural network architectures.

    PubMed

    Fasel, B

    2003-03-01

    In this introduction to artificial neural networks we attempt to give an overview of the most important types of neural networks employed in engineering and explain shortly how they operate and also how they relate to biological neural networks. The focus will mainly be on bio-inspired artificial neural network architectures and specifically to neo-perceptions. The latter belong to the family of convolutional neural networks. Their topology is somewhat similar to the one of the human visual cortex and they are based on receptive fields that allow, in combination with sub-sampling layers, for an improved robustness with regard to local spatial distortions. We demonstrate the application of artificial neural networks to face analysis--a domain we human beings are particularly good at, yet which poses great difficulties for digital computers running deterministic software programs.

  8. The development of brain network architecture.

    PubMed

    Wierenga, Lara M; van den Heuvel, Martijn P; van Dijk, Sarai; Rijks, Yvonne; de Reus, Marcel A; Durston, Sarah

    2016-02-01

    Brain connectivity shows protracted development throughout childhood and adolescence, and, as such, the topology of brain networks changes during this period. The complexity of these changes with development is reflected by regional differences in maturation. This study explored age-related changes in network topology and regional developmental patterns during childhood and adolescence. We acquired two sets of Diffusion Weighted Imaging-scans and anatomical T1-weighted scans. The first dataset included 85 typically developing individuals (53 males; 32 females), aged between 7 and 23 years and was acquired on a Philips Achieva 1.5 Tesla scanner. A second dataset (N = 38) was acquired on a different (but identical) 1.5 T scanner and was used for independent replication of our results. We reconstructed whole brain networks using tractography. We operationalized fiber tract development as changes in mean diffusivity and radial diffusivity with age. Most fibers showed maturational changes in mean and radial diffusivity values throughout childhood and adolescence, likely reflecting increasing white matter integrity. The largest age-related changes were observed in association fibers within and between the frontal and parietal lobes. Furthermore, there was a simultaneous age-related decrease in average path length (P < 0.0001), increase in node strength (P < 0.0001) as well as network clustering (P = 0.001), which may reflect fine-tuning of topological organization. These results suggest a sequential maturational model where connections between unimodal regions strengthen in childhood, followed by connections from these unimodal regions to association regions, while adolescence is characterized by the strengthening of connections between association regions within the frontal and parietal cortex. Hum Brain Mapp 37:717-729, 2016. © 2015 Wiley Periodicals, Inc.

  9. Neural network architectures to analyze OPAD data

    NASA Technical Reports Server (NTRS)

    Whitaker, Kevin W.

    1992-01-01

    A prototype Optical Plume Anomaly Detection (OPAD) system is now installed on the space shuttle main engine (SSME) Technology Test Bed (TTB) at MSFC. The OPAD system requirements dictate the need for fast, efficient data processing techniques. To address this need of the OPAD system, a study was conducted into how artificial neural networks could be used to assist in the analysis of plume spectral data.

  10. Actin network architecture can determine myosin motor activity.

    PubMed

    Reymann, Anne-Cécile; Boujemaa-Paterski, Rajaa; Martiel, Jean-Louis; Guérin, Christophe; Cao, Wenxiang; Chin, Harvey F; De La Cruz, Enrique M; Théry, Manuel; Blanchoin, Laurent

    2012-06-08

    The organization of actin filaments into higher-ordered structures governs eukaryotic cell shape and movement. Global actin network size and architecture are maintained in a dynamic steady state through regulated assembly and disassembly. Here, we used experimentally defined actin structures in vitro to investigate how the activity of myosin motors depends on network architecture. Direct visualization of filaments revealed myosin-induced actin network deformation. During this reorganization, myosins selectively contracted and disassembled antiparallel actin structures, while parallel actin bundles remained unaffected. The local distribution of nucleation sites and the resulting orientation of actin filaments appeared to regulate the scalability of the contraction process. This "orientation selection" mechanism for selective contraction and disassembly suggests how the dynamics of the cellular actin cytoskeleton can be spatially controlled by actomyosin contractility.

  11. Development of the network architecture of the Canadian MSAT system

    NASA Technical Reports Server (NTRS)

    Davies, N. George; Shoamanesh, Alireza; Leung, Victor C. M.

    1988-01-01

    A description is given of the present concept for the Canadian Mobile Satellite (MSAT) System and the development of the network architecture which will accommodate the planned family of three categories of service: a mobile radio service (MRS), a mobile telephone service (MTS), and a mobile data service (MDS). The MSAT satellite will have cross-strapped L-band and Ku-band transponders to provide communications services between L-band mobile terminals and fixed base stations supporting dispatcher-type MRS, gateway stations supporting MTS interconnections to the public telephone network, data hub stations supporting the MDS, and the network control center. The currently perceived centralized architecture with demand assignment multiple access for the circuit switched MRS, MTS and permanently assigned channels for the packet switched MDS is discussed.

  12. Tracing the Flow of Perceptual Features in an Algorithmic Brain Network

    PubMed Central

    Ince, Robin A. A.; van Rijsbergen, Nicola J.; Thut, Gregor; Rousselet, Guillaume A.; Gross, Joachim; Panzeri, Stefano; Schyns, Philippe G.

    2015-01-01

    The model of the brain as an information processing machine is a profound hypothesis in which neuroscience, psychology and theory of computation are now deeply rooted. Modern neuroscience aims to model the brain as a network of densely interconnected functional nodes. However, to model the dynamic information processing mechanisms of perception and cognition, it is imperative to understand brain networks at an algorithmic level–i.e. as the information flow that network nodes code and communicate. Here, using innovative methods (Directed Feature Information), we reconstructed examples of possible algorithmic brain networks that code and communicate the specific features underlying two distinct perceptions of the same ambiguous picture. In each observer, we identified a network architecture comprising one occipito-temporal hub where the features underlying both perceptual decisions dynamically converge. Our focus on detailed information flow represents an important step towards a new brain algorithmics to model the mechanisms of perception and cognition. PMID:26635299

  13. Simulating Visual Learning and Optical Illusions via a Network-Based Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Siu, Theodore; Vivar, Miguel; Shinbrot, Troy

    We present a neural network model that uses a genetic algorithm to identify spatial patterns. We show that the model both learns and reproduces common visual patterns and optical illusions. Surprisingly, we find that the illusions generated are a direct consequence of the network architecture used. We discuss the implications of our results and the insights that we gain on how humans fall for optical illusions

  14. Algorithms for parallel flow solvers on message passing architectures

    NASA Technical Reports Server (NTRS)

    Vanderwijngaart, Rob F.

    1995-01-01

    The purpose of this project has been to identify and test suitable technologies for implementation of fluid flow solvers -- possibly coupled with structures and heat equation solvers -- on MIMD parallel computers. In the course of this investigation much attention has been paid to efficient domain decomposition strategies for ADI-type algorithms. Multi-partitioning derives its efficiency from the assignment of several blocks of grid points to each processor in the parallel computer. A coarse-grain parallelism is obtained, and a near-perfect load balance results. In uni-partitioning every processor receives responsibility for exactly one block of grid points instead of several. This necessitates fine-grain pipelined program execution in order to obtain a reasonable load balance. Although fine-grain parallelism is less desirable on many systems, especially high-latency networks of workstations, uni-partition methods are still in wide use in production codes for flow problems. Consequently, it remains important to achieve good efficiency with this technique that has essentially been superseded by multi-partitioning for parallel ADI-type algorithms. Another reason for the concentration on improving the performance of pipeline methods is their applicability in other types of flow solver kernels with stronger implied data dependence. Analytical expressions can be derived for the size of the dynamic load imbalance incurred in traditional pipelines. From these it can be determined what is the optimal first-processor retardation that leads to the shortest total completion time for the pipeline process. Theoretical predictions of pipeline performance with and without optimization match experimental observations on the iPSC/860 very well. Analysis of pipeline performance also highlights the effect of uncareful grid partitioning in flow solvers that employ pipeline algorithms. If grid blocks at boundaries are not at least as large in the wall-normal direction as those

  15. Network architecture in a converged optical + IP network

    NASA Astrophysics Data System (ADS)

    Wakim, Walid; Zottmann, Harald

    2012-01-01

    As demands on Provider Networks continue to grow at exponential rates, providers are forced to evaluate how to continue to grow the network while increasing service velocity, enhancing resiliency while decreasing the total cost of ownership (TCO). The bandwidth growth that networks are experiencing is in the form packet based multimedia services such as video, video conferencing, gaming, etc... mixed with Over the Top (OTT) content providers such as Netflix, and the customer's expectations that best effort is not enough you end up with a situation that forces the provider to analyze how to gain more out of the network with less cost. In this paper we will discuss changes in the network that are driving us to a tighter integration between packet and optical layers and how to improve on today's multi - layer inefficiencies to drive down network TCO and provide for a fully integrated and dynamic network that will decrease time to revenue.

  16. Neural Network Architectures for General Image Recognition.

    DTIC Science & Technology

    1992-07-21

    Design Procedure 88 A.5 Examples 92 A.6 Scaling Laws for Cooperative-Competitive Neural Networks 98 A.7 Discussion 100 REFERENCES 101 viii LIST OF...control law for vertical pull-in of the window. The two V2 outputs, NORTH and SOUTH, are subtracted and the window is moved if the thresholds are exceeded...An input pattern, shown in cross-hatching, is irapressed on M neurons. Each input neuron is connected to N hidden neutons and a single output neuron

  17. A network architecture for Petaflops supercomputers.

    SciTech Connect

    DeBenedictis, Erik P.

    2003-09-01

    If we are to build a supercomputer with a speed of 10{sup 15} floating operations per second (1 PetaFLOPS), interconnect technology will need to be improved considerably over what it is today. In this report, we explore one possible interconnect design for such a network. The guiding principle in this design is the optimization of all components for the finiteness of the speed of light. To achieve a linear speedup in time over well-tested supercomputers of todays' designs will require scaling up of processor power and bandwidth and scaling down of latency. Latency scaling is the most challenging: it requires a 100 ns user-to-user latency for messages traveling the full diameter of the machine. To meet this constraint requires simultaneously minimizing wire length through 3D packaging, new low-latency electrical signaling mechanisms, extremely fast routers, and new network interfaces. In this report, we outline approaches and implementations that will meet the requirements when implemented as a system. No technology breakthroughs are required.

  18. Underwater Sensor Network Redeployment Algorithm Based on Wolf Search

    PubMed Central

    Jiang, Peng; Feng, Yang; Wu, Feng

    2016-01-01

    This study addresses the optimization of node redeployment coverage in underwater wireless sensor networks. Given that nodes could easily become invalid under a poor environment and the large scale of underwater wireless sensor networks, an underwater sensor network redeployment algorithm was developed based on wolf search. This study is to apply the wolf search algorithm combined with crowded degree control in the deployment of underwater wireless sensor networks. The proposed algorithm uses nodes to ensure coverage of the events, and it avoids the prematurity of the nodes. The algorithm has good coverage effects. In addition, considering that obstacles exist in the underwater environment, nodes are prevented from being invalid by imitating the mechanism of avoiding predators. Thus, the energy consumption of the network is reduced. Comparative analysis shows that the algorithm is simple and effective in wireless sensor network deployment. Compared with the optimized artificial fish swarm algorithm, the proposed algorithm exhibits advantages in network coverage, energy conservation, and obstacle avoidance. PMID:27775659

  19. Social network architecture and the maintenance of deleterious cultural traits

    PubMed Central

    Yeaman, Sam; Schick, Alana; Lehmann, Laurent

    2012-01-01

    How have changes in communications technology affected the way that misinformation spreads through a population and persists? To what extent do differences in the architecture of social networks affect the spread of misinformation, relative to the rates and rules by which individuals transmit or eliminate different pieces of information (cultural traits)? Here, we use analytical models and individual-based simulations to study how a ‘cultural load’ of misinformation can be maintained in a population under a balance between social transmission and selective elimination of cultural traits with low intrinsic value. While considerable research has explored how network architecture affects percolation processes, we find that the relative rates at which individuals transmit or eliminate traits can have much more profound impacts on the cultural load than differences in network architecture. In particular, the cultural load is insensitive to correlations between an individual's network degree and rate of elimination when these quantities vary among individuals. Taken together, these results suggest that changes in communications technology may have influenced cultural evolution more strongly through changes in the amount of information flow, rather than the details of who is connected to whom. PMID:22031730

  20. Social network architecture and the maintenance of deleterious cultural traits.

    PubMed

    Yeaman, Sam; Schick, Alana; Lehmann, Laurent

    2012-05-07

    How have changes in communications technology affected the way that misinformation spreads through a population and persists? To what extent do differences in the architecture of social networks affect the spread of misinformation, relative to the rates and rules by which individuals transmit or eliminate different pieces of information (cultural traits)? Here, we use analytical models and individual-based simulations to study how a 'cultural load' of misinformation can be maintained in a population under a balance between social transmission and selective elimination of cultural traits with low intrinsic value. While considerable research has explored how network architecture affects percolation processes, we find that the relative rates at which individuals transmit or eliminate traits can have much more profound impacts on the cultural load than differences in network architecture. In particular, the cultural load is insensitive to correlations between an individual's network degree and rate of elimination when these quantities vary among individuals. Taken together, these results suggest that changes in communications technology may have influenced cultural evolution more strongly through changes in the amount of information flow, rather than the details of who is connected to whom.

  1. A genetic algorithm for solving supply chain network design model

    NASA Astrophysics Data System (ADS)

    Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.

    2013-09-01

    Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.

  2. Strategies for concurrent processing of complex algorithms in data driven architectures

    NASA Technical Reports Server (NTRS)

    Som, Sukhamoy; Stoughton, John W.; Mielke, Roland R.

    1990-01-01

    Performance modeling and performance enhancement for periodic execution of large-grain, decision-free algorithms in data flow architectures are discussed. Applications include real-time implementation of control and signal processing algorithms where performance is required to be highly predictable. The mapping of algorithms onto the specified class of data flow architectures is realized by a marked graph model called algorithm to architecture mapping model (ATAMM). Performance measures and bounds are established. Algorithm transformation techniques are identified for performance enhancement and reduction of resource (computing element) requirements. A systematic design procedure is described for generating operating conditions for predictable performance both with and without resource constraints. An ATAMM simulator is used to test and validate the performance prediction by the design procedure. Experiments on a three resource testbed provide verification of the ATAMM model and the design procedure.

  3. Strategies for concurrent processing of complex algorithms in data driven architectures

    NASA Technical Reports Server (NTRS)

    Stoughton, John W.; Mielke, Roland R.; Som, Sukhamony

    1990-01-01

    The performance modeling and enhancement for periodic execution of large-grain, decision-free algorithms in data flow architectures is examined. Applications include real-time implementation of control and signal processing algorithms where performance is required to be highly predictable. The mapping of algorithms onto the specified class of data flow architectures is realized by a marked graph model called ATAMM (Algorithm To Architecture Mapping Model). Performance measures and bounds are established. Algorithm transformation techniques are identified for performance enhancement and reduction of resource (computing element) requirements. A systematic design procedure is described for generating operating conditions for predictable performance both with and without resource constraints. An ATAMM simulator is used to test and validate the performance prediction by the design procedure. Experiments on a three resource testbed provide verification of the ATAMM model and the design procedure.

  4. Evolution of network architecture in a granular material under compression

    NASA Astrophysics Data System (ADS)

    Papadopoulos, Lia; Puckett, James G.; Daniels, Karen E.; Bassett, Danielle S.

    2016-09-01

    As a granular material is compressed, the particles and forces within the system arrange to form complex and heterogeneous collective structures. Force chains are a prime example of such structures, and are thought to constrain bulk properties such as mechanical stability and acoustic transmission. However, capturing and characterizing the evolving nature of the intrinsic inhomogeneity and mesoscale architecture of granular systems can be challenging. A growing body of work has shown that graph theoretic approaches may provide a useful foundation for tackling these problems. Here, we extend the current approaches by utilizing multilayer networks as a framework for directly quantifying the progression of mesoscale architecture in a compressed granular system. We examine a quasi-two-dimensional aggregate of photoelastic disks, subject to biaxial compressions through a series of small, quasistatic steps. Treating particles as network nodes and interparticle forces as network edges, we construct a multilayer network for the system by linking together the series of static force networks that exist at each strain step. We then extract the inherent mesoscale structure from the system by using a generalization of community detection methods to multilayer networks, and we define quantitative measures to characterize the changes in this structure throughout the compression process. We separately consider the network of normal and tangential forces, and find that they display a different progression throughout compression. To test the sensitivity of the network model to particle properties, we examine whether the method can distinguish a subsystem of low-friction particles within a bath of higher-friction particles. We find that this can be achieved by considering the network of tangential forces, and that the community structure is better able to separate the subsystem than a purely local measure of interparticle forces alone. The results discussed throughout this study

  5. Traffic sharing algorithms for hybrid mobile networks

    NASA Technical Reports Server (NTRS)

    Arcand, S.; Murthy, K. M. S.; Hafez, R.

    1995-01-01

    In a hybrid (terrestrial + satellite) mobile personal communications networks environment, a large size satellite footprint (supercell) overlays on a large number of smaller size, contiguous terrestrial cells. We assume that the users have either a terrestrial only single mode terminal (SMT) or a terrestrial/satellite dual mode terminal (DMT) and the ratio of DMT to the total terminals is defined gamma. It is assumed that the call assignments to and handovers between terrestrial cells and satellite supercells take place in a dynamic fashion when necessary. The objectives of this paper are twofold, (1) to propose and define a class of traffic sharing algorithms to manage terrestrial and satellite network resources efficiently by handling call handovers dynamically, and (2) to analyze and evaluate the algorithms by maximizing the traffic load handling capability (defined in erl/cell) over a wide range of terminal ratios (gamma) given an acceptable range of blocking probabilities. Two of the algorithms (G & S) in the proposed class perform extremely well for a wide range of gamma.

  6. Clustering algorithm for determining community structure in large networks

    NASA Astrophysics Data System (ADS)

    Pujol, Josep M.; Béjar, Javier; Delgado, Jordi

    2006-07-01

    We propose an algorithm to find the community structure in complex networks based on the combination of spectral analysis and modularity optimization. The clustering produced by our algorithm is as accurate as the best algorithms on the literature of modularity optimization; however, the main asset of the algorithm is its efficiency. The best match for our algorithm is Newman’s fast algorithm, which is the reference algorithm for clustering in large networks due to its efficiency. When both algorithms are compared, our algorithm outperforms the fast algorithm both in efficiency and accuracy of the clustering, in terms of modularity. Thus, the results suggest that the proposed algorithm is a good choice to analyze the community structure of medium and large networks in the range of tens and hundreds of thousand vertices.

  7. Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique

    SciTech Connect

    Dumidu Wijayasekara; Milos Manic; Piyush Sabharwall; Vivek Utgikar

    2011-07-01

    Artificial Neural Networks (ANN) have been used in the past to predict the performance of printed circuit heat exchangers (PCHE) with satisfactory accuracy. Typically published literature has focused on optimizing ANN using a training dataset to train the network and a testing dataset to evaluate it. Although this may produce outputs that agree with experimental results, there is a risk of over-training or overlearning the network rather than generalizing it, which should be the ultimate goal. An over-trained network is able to produce good results with the training dataset but fails when new datasets with subtle changes are introduced. In this paper we present EBaLM-OTR (error back propagation and Levenberg-Marquardt algorithms for over training resilience) technique, which is based on a previously discussed method of selecting neural network architecture that uses a separate validation set to evaluate different network architectures based on mean square error (MSE), and standard deviation of MSE. The method uses k-fold cross validation. Therefore in order to select the optimal architecture for the problem, the dataset is divided into three parts which are used to train, validate and test each network architecture. Then each architecture is evaluated according to their generalization capability and capability to conform to original data. The method proved to be a comprehensive tool in identifying the weaknesses and advantages of different network architectures. The method also highlighted the fact that the architecture with the lowest training error is not always the most generalized and therefore not the optimal. Using the method the testing error achieved was in the order of magnitude of within 10{sup -5} - 10{sup -3}. It was also show that the absolute error achieved by EBaLM-OTR was an order of magnitude better than the lowest error achieved by EBaLM-THP.

  8. High-performance, scalable optical network-on-chip architectures

    NASA Astrophysics Data System (ADS)

    Tan, Xianfang

    The rapid advance of technology enables a large number of processing cores to be integrated into a single chip which is called a Chip Multiprocessor (CMP) or a Multiprocessor System-on-Chip (MPSoC) design. The on-chip interconnection network, which is the communication infrastructure for these processing cores, plays a central role in a many-core system. With the continuously increasing complexity of many-core systems, traditional metallic wired electronic networks-on-chip (NoC) became a bottleneck because of the unbearable latency in data transmission and extremely high energy consumption on chip. Optical networks-on-chip (ONoC) has been proposed as a promising alternative paradigm for electronic NoC with the benefits of optical signaling communication such as extremely high bandwidth, negligible latency, and low power consumption. This dissertation focus on the design of high-performance and scalable ONoC architectures and the contributions are highlighted as follow: 1. A micro-ring resonator (MRR)-based Generic Wavelength-routed Optical Router (GWOR) is proposed. A method for developing any sized GWOR is introduced. GWOR is a scalable non-blocking ONoC architecture with simple structure, low cost and high power efficiency compared to existing ONoC designs. 2. To expand the bandwidth and improve the fault tolerance of the GWOR, a redundant GWOR architecture is designed by cascading different type of GWORs into one network. 3. The redundant GWOR built with MRR-based comb switches is proposed. Comb switches can expand the bandwidth while keep the topology of GWOR unchanged by replacing the general MRRs with comb switches. 4. A butterfly fat tree (BFT)-based hybrid optoelectronic NoC (HONoC) architecture is developed in which GWORs are used for global communication and electronic routers are used for local communication. The proposed HONoC uses less numbers of electronic routers and links than its counterpart of electronic BFT-based NoC. It takes the advantages of

  9. Advancing reversible shape memory by tuning the polymer network architecture

    SciTech Connect

    Li, Qiaoxi; Zhou, Jing; Vatankhah-Varnoosfaderani, Mohammad; Nykypanchuk, Dmytro; Gang, Oleg; Sheiko, Sergei S.

    2016-02-02

    Because of counteraction of a chemical network and a crystalline scaffold, semicrystalline polymer networks exhibit a peculiar behavior—reversible shape memory (RSM), which occurs naturally without applying any external force and particular structural design. There are three RSM properties: (i) range of reversible strain, (ii) rate of strain recovery, and (iii) decay of reversibility with time, which can be improved by tuning the architecture of the polymer network. Different types of poly(octylene adipate) networks were synthesized, allowing for control of cross-link density and network topology, including randomly cross-linked network by free-radical polymerization, thiol–ene clicked network with enhanced mesh uniformity, and loose network with deliberately incorporated dangling chains. It is shown that the RSM properties are controlled by average cross-link density and crystal size, whereas topology of a network greatly affects its extensibility. In conclusion, we have achieved 80% maximum reversible range, 15% minimal decrease in reversibility, and fast strain recovery rate up to 0.05 K–1, i.e., ca. 5% per 10 s at a cooling rate of 5 K/min.

  10. Advancing reversible shape memory by tuning the polymer network architecture

    DOE PAGES

    Li, Qiaoxi; Zhou, Jing; Vatankhah-Varnoosfaderani, Mohammad; ...

    2016-02-02

    Because of counteraction of a chemical network and a crystalline scaffold, semicrystalline polymer networks exhibit a peculiar behavior—reversible shape memory (RSM), which occurs naturally without applying any external force and particular structural design. There are three RSM properties: (i) range of reversible strain, (ii) rate of strain recovery, and (iii) decay of reversibility with time, which can be improved by tuning the architecture of the polymer network. Different types of poly(octylene adipate) networks were synthesized, allowing for control of cross-link density and network topology, including randomly cross-linked network by free-radical polymerization, thiol–ene clicked network with enhanced mesh uniformity, and loosemore » network with deliberately incorporated dangling chains. It is shown that the RSM properties are controlled by average cross-link density and crystal size, whereas topology of a network greatly affects its extensibility. In conclusion, we have achieved 80% maximum reversible range, 15% minimal decrease in reversibility, and fast strain recovery rate up to 0.05 K–1, i.e., ca. 5% per 10 s at a cooling rate of 5 K/min.« less

  11. Shifts in the architecture of the Nationwide Health Information Network.

    PubMed

    Lenert, Leslie; Sundwall, David; Lenert, Michael Edward

    2012-01-01

    In the midst of a US $30 billion USD investment in the Nationwide Health Information Network (NwHIN) and electronic health records systems, a significant change in the architecture of the NwHIN is taking place. Prior to 2010, the focus of information exchange in the NwHIN was the Regional Health Information Organization (RHIO). Since 2010, the Office of the National Coordinator (ONC) has been sponsoring policies that promote an internet-like architecture that encourages point to-point information exchange and private health information exchange networks. The net effect of these activities is to undercut the limited business model for RHIOs, decreasing the likelihood of their success, while making the NwHIN dependent on nascent technologies for community level functions such as record locator services. These changes may impact the health of patients and communities. Independent, scientifically focused debate is needed on the wisdom of ONC's proposed changes in its strategy for the NwHIN.

  12. The Snowcloud System: Architecture and Algorithms for Snow Hydrology Studies

    NASA Astrophysics Data System (ADS)

    Skalka, C.; Brown, I.; Frolik, J.

    2013-12-01

    Snowcloud is an embedded data collection system for snow hydrology field research campaigns conducted in harsh climates and remote areas. The system combines distributed wireless sensor network technology and computational techniques to provide data at lower cost and higher spatio-temporal resolution than ground-based systems using traditional methods. Snowcloud has seen multiple Winter deployments in settings ranging from high desert to arctic, resulting in over a dozen node-years of practical experience. The Snowcloud system architecture consists of multiple TinyOS mesh-networked sensor stations collecting environmental data above and, in some deployments, below the snowpack. Monitored data modalities include snow depth, ground and air temperature, PAR and leaf-area index (LAI), and soil moisture. To enable power cycling and control of multiple sensors a custom power and sensor conditioning board was developed. The electronics and structural systems for individual stations have been designed and tested (in the lab and in situ) for ease of assembly and robustness to harsh winter conditions. Battery systems and solar chargers enable seasonal operation even under low/no light arctic conditions. Station costs range between 500 and 1000 depending on the instrumentation suite. For remote field locations, a custom designed hand-held device and data retrieval protocol serves as the primary data collection method. We are also developing and testing a Gateway device that will report data in near-real-time (NRT) over a cellular connection. Data is made available to users via web interfaces that also provide basic data analysis and visualization tools. For applications to snow hydrology studies, the better spatiotemporal resolution of snowpack data provided by Snowcloud is beneficial in several aspects. It provides insight into snowpack evolution, and allows us to investigate differences across different spatial and temporal scales in deployment areas. It enables the

  13. Strategies for concurrent processing of complex algorithms in data driven architectures

    NASA Technical Reports Server (NTRS)

    Stoughton, John W.; Mielke, Roland R.

    1987-01-01

    The results of ongoing research directed at developing a graph theoretical model for describing data and control flow associated with the execution of large grained algorithms in a spatial distributed computer environment is presented. This model is identified by the acronym ATAMM (Algorithm/Architecture Mapping Model). The purpose of such a model is to provide a basis for establishing rules for relating an algorithm to its execution in a multiprocessor environment. Specifications derived from the model lead directly to the description of a data flow architecture which is a consequence of the inherent behavior of the data and control flow described by the model. The purpose of the ATAMM based architecture is to optimize computational concurrency in the multiprocessor environment and to provide an analytical basis for performance evaluation. The ATAMM model and architecture specifications are demonstrated on a prototype system for concept validation.

  14. Strategies for concurrent processing of complex algorithms in data driven architectures

    NASA Technical Reports Server (NTRS)

    Stoughton, John W.; Mielke, Roland R.

    1988-01-01

    Research directed at developing a graph theoretical model for describing data and control flow associated with the execution of large grained algorithms in a special distributed computer environment is presented. This model is identified by the acronym ATAMM which represents Algorithms To Architecture Mapping Model. The purpose of such a model is to provide a basis for establishing rules for relating an algorithm to its execution in a multiprocessor environment. Specifications derived from the model lead directly to the description of a data flow architecture which is a consequence of the inherent behavior of the data and control flow described by the model. The purpose of the ATAMM based architecture is to provide an analytical basis for performance evaluation. The ATAMM model and architecture specifications are demonstrated on a prototype system for concept validation.

  15. Fast packet switch architectures for broadband integrated services digital networks

    NASA Technical Reports Server (NTRS)

    Tobagi, Fouad A.

    1990-01-01

    Background information on networking and switching is provided, and the various architectures that have been considered for fast packet switches are described. The focus is solely on switches designed to be implemented electronically. A set of definitions and a brief description of the functionality required of fast packet switches are given. Three basic types of packet switches are identified: the shared-memory, shared-medium, and space-division types. Each of these is described, and examples are given.

  16. A Holistic Management Architecture for Large-Scale Adaptive Networks

    DTIC Science & Technology

    2007-09-01

    MANAGEMENT ARCHITECTURE FOR LARGE-SCALE ADAPTIVE NETWORKS by Michael R. Clement September 2007 Thesis Advisor: Alex Bordetsky Second Reader...TECHNOLOGY MANAGEMENT from the NAVAL POSTGRADUATE SCHOOL September 2007 Author: Michael R. Clement Approved by: Dr. Alex ...achieve in life is by His will. Ad Majorem Dei Gloriam. To my parents, my family, and Caitlin: For supporting me, listening to me when I got

  17. A multi-agent system architecture for sensor networks.

    PubMed

    Fuentes-Fernández, Rubén; Guijarro, María; Pajares, Gonzalo

    2009-01-01

    The design of the control systems for sensor networks presents important challenges. Besides the traditional problems about how to process the sensor data to obtain the target information, engineers need to consider additional aspects such as the heterogeneity and high number of sensors, and the flexibility of these networks regarding topologies and the sensors in them. Although there are partial approaches for resolving these issues, their integration relies on ad hoc solutions requiring important development efforts. In order to provide an effective approach for this integration, this paper proposes an architecture based on the multi-agent system paradigm with a clear separation of concerns. The architecture considers sensors as devices used by an upper layer of manager agents. These agents are able to communicate and negotiate services to achieve the required functionality. Activities are organized according to roles related with the different aspects to integrate, mainly sensor management, data processing, communication and adaptation to changes in the available devices and their capabilities. This organization largely isolates and decouples the data management from the changing network, while encouraging reuse of solutions. The use of the architecture is facilitated by a specific modelling language developed through metamodelling. A case study concerning a generic distributed system for fire fighting illustrates the approach and the comparison with related work.

  18. A Multi-Agent System Architecture for Sensor Networks

    PubMed Central

    Fuentes-Fernández, Rubén; Guijarro, María; Pajares, Gonzalo

    2009-01-01

    The design of the control systems for sensor networks presents important challenges. Besides the traditional problems about how to process the sensor data to obtain the target information, engineers need to consider additional aspects such as the heterogeneity and high number of sensors, and the flexibility of these networks regarding topologies and the sensors in them. Although there are partial approaches for resolving these issues, their integration relies on ad hoc solutions requiring important development efforts. In order to provide an effective approach for this integration, this paper proposes an architecture based on the multi-agent system paradigm with a clear separation of concerns. The architecture considers sensors as devices used by an upper layer of manager agents. These agents are able to communicate and negotiate services to achieve the required functionality. Activities are organized according to roles related with the different aspects to integrate, mainly sensor management, data processing, communication and adaptation to changes in the available devices and their capabilities. This organization largely isolates and decouples the data management from the changing network, while encouraging reuse of solutions. The use of the architecture is facilitated by a specific modelling language developed through metamodelling. A case study concerning a generic distributed system for fire fighting illustrates the approach and the comparison with related work. PMID:22303172

  19. Integrated Network Architecture for Sustained Human and Robotic Exploration

    NASA Technical Reports Server (NTRS)

    Noreen, Gary; Cesarone, Robert; Deutsch, Leslie; Edwards, Charles; Soloff, Jason; Ely, Todd; Cook, Brian; Morabito, David; Hemmati, Hamid; Piazolla, Sabino; Hastrup, Rolf; Abraham, Douglas; Miles, Sue; Manshadi, Farzin

    2005-01-01

    The National Aeronautics and Space Administration (NASA) Exploration Systems Enterprise is planning a series of human and robotic missions to the Earth's moon and to Mars. These missions will require communication and navigation services. This paper1 sets forth presumed requirements for such services and concepts for lunar and Mars telecommunications network architectures to satisfy the presumed requirements. The paper suggests that an inexpensive ground network would suffice for missions to the near-side of the moon. A constellation of three Lunar Telecommunications Orbiters connected to an inexpensive ground network could provide continuous redundant links to a polar lunar base and its vicinity. For human and robotic missions to Mars, a pair of areostationary satellites could provide continuous redundant links between Earth and a mid-latitude Mars base in conjunction with the Deep Space Network augmented by large arrays of 12-m antennas on Earth.

  20. Study of data fusion algorithms applied to unattended ground sensor network

    NASA Astrophysics Data System (ADS)

    Pannetier, B.; Moras, J.; Dezert, Jean; Sella, G.

    2014-06-01

    In this paper, data obtained from wireless unattended ground sensor network are used for tracking multiple ground targets (vehicles, pedestrians and animals) moving on and off the road network. The goal of the study is to evaluate several data fusion algorithms to select the best approach to establish the tactical situational awareness. The ground sensor network is composed of heterogeneous sensors (optronic, radar, seismic, acoustic, magnetic sensors) and data fusion nodes. The fusion nodes are small hardware platforms placed on the surveillance area that communicate together. In order to satisfy operational needs and the limited communication bandwidth between the nodes, we study several data fusion algorithms to track and classify targets in real time. A multiple targets tracking (MTT) algorithm is integrated in each data fusion node taking into account embedded constraint. The choice of the MTT algorithm is motivated by the limit of the chosen technology. In the fusion nodes, the distributed MTT algorithm exploits the road network information in order to constrain the multiple dynamic models. Then, a variable structure interacting multiple model (VS-IMM) is adapted with the road network topology. This algorithm is well-known in centralized architecture, but it implies a modification of other data fusion algorithms to preserve the performances of the tracking under constraints. Based on such VS-IMM MTT algorithm, we adapt classical data fusion techniques to make it working in three architectures: centralized, distributed and hierarchical. The sensors measurements are considered asynchronous, but the fusion steps are synchronized on all sensors. Performances of data fusion algorithms are evaluated using simulated data and also validated on real data. The scenarios under analysis contain multiple targets with close and crossing trajectories involving data association uncertainties.

  1. Differentiated service in OBS networks using a dynamic FDL bank partitioning algorithm.

    PubMed

    Yonggyu, Lee; Kim, Namuk; Kim, Jaegwan; Ahn, Junseop; Kang, Minho

    2007-05-14

    In order to solve the differentiated service problem in optical burst switching (OBS) networks, we propose a dynamic fiber delay line (FDL) bank partitioning algorithm, which divides a FDL bank into several groups, using a feed-forward output buffering architecture. In the analysis, three classes and groups are considered for traffic and FDL, respectively, and each group is assigned to each class. This paper shows that the loss differentiation in OBS networks is easily accomplished in Poisson traffic environments when our dynamic algorithm is adopted.

  2. DFT algorithms for bit-serial GaAs array processor architectures

    NASA Technical Reports Server (NTRS)

    Mcmillan, Gary B.

    1988-01-01

    Systems and Processes Engineering Corporation (SPEC) has developed an innovative array processor architecture for computing Fourier transforms and other commonly used signal processing algorithms. This architecture is designed to extract the highest possible array performance from state-of-the-art GaAs technology. SPEC's architectural design includes a high performance RISC processor implemented in GaAs, along with a Floating Point Coprocessor and a unique Array Communications Coprocessor, also implemented in GaAs technology. Together, these data processors represent the latest in technology, both from an architectural and implementation viewpoint. SPEC has examined numerous algorithms and parallel processing architectures to determine the optimum array processor architecture. SPEC has developed an array processor architecture with integral communications ability to provide maximum node connectivity. The Array Communications Coprocessor embeds communications operations directly in the core of the processor architecture. A Floating Point Coprocessor architecture has been defined that utilizes Bit-Serial arithmetic units, operating at very high frequency, to perform floating point operations. These Bit-Serial devices reduce the device integration level and complexity to a level compatible with state-of-the-art GaAs device technology.

  3. Fiber-Optic Network Architectures for Onboard Avionics Applications Investigated

    NASA Technical Reports Server (NTRS)

    Nguyen, Hung D.; Ngo, Duc H.

    2003-01-01

    This project is part of a study within the Advanced Air Transportation Technologies program undertaken at the NASA Glenn Research Center. The main focus of the program is the improvement of air transportation, with particular emphasis on air transportation safety. Current and future advances in digital data communications between an aircraft and the outside world will require high-bandwidth onboard communication networks. Radiofrequency (RF) systems, with their interconnection network based on coaxial cables and waveguides, increase the complexity of communication systems onboard modern civil and military aircraft with respect to weight, power consumption, and safety. In addition, safety and reliability concerns from electromagnetic interference between the RF components embedded in these communication systems exist. A simple, reliable, and lightweight network that is free from the effects of electromagnetic interference and capable of supporting the broadband communications needs of future onboard digital avionics systems cannot be easily implemented using existing coaxial cable-based systems. Fiber-optical communication systems can meet all these challenges of modern avionics applications in an efficient, cost-effective manner. The objective of this project is to present a number of optical network architectures for onboard RF signal distribution. Because of the emergence of a number of digital avionics devices requiring high-bandwidth connectivity, fiber-optic RF networks onboard modern aircraft will play a vital role in ensuring a low-noise, highly reliable RF communication system. Two approaches are being used for network architectures for aircraft onboard fiber-optic distribution systems: a hybrid RF-optical network and an all-optical wavelength division multiplexing (WDM) network.

  4. Random Evolution of Idiotypic Networks: Dynamics and Architecture

    NASA Astrophysics Data System (ADS)

    Brede, Markus; Behn, Ulrich

    The paper deals with modelling a subsystem of the immune system, the so-called idiotypic network (INW). INWs, conceived by N.K. Jerne in 1974, are functional networks of interacting antibodies and B cells. In principle, Jernes' framework provides solutions to many issues in immunology, such as immunological memory, mechanisms for antigen recognition and self/non-self discrimination. Explaining the interconnection between the elementary components, local dynamics, network formation and architecture, and possible modes of global system function appears to be an ideal playground of statistical mechanics. We present a simple cellular automaton model, based on a graph representation of the system. From a simplified description of idiotypic interactions, rules for the random evolution of networks of occupied and empty sites on these graphs are derived. In certain biologically relevant parameter ranges the resultant dynamics leads to stationary states. A stationary state is found to correspond to a specific pattern of network organization. It turns out that even these very simple rules give rise to a multitude of different kinds of patterns. We characterize these networks by classifying `static' and `dynamic' network-patterns. A type of `dynamic' network is found to display many features of real INWs.

  5. Network Algorithms for Detection of Radiation Sources

    SciTech Connect

    Rao, Nageswara S; Brooks, Richard R; Wu, Qishi

    2014-01-01

    In support of national defense, Domestic Nuclear Detection Office s (DNDO) Intelligent Radiation Sensor Systems (IRSS) program supported the development of networks of radiation counters for detecting, localizing and identifying low-level, hazardous radiation sources. Industry teams developed the first generation of such networks with tens of counters, and demonstrated several of their capabilities in indoor and outdoor characterization tests. Subsequently, these test measurements have been used in algorithm replays using various sub-networks of counters. Test measurements combined with algorithm outputs are used to extract Key Measurements and Benchmark (KMB) datasets. We present two selective analyses of these datasets: (a) a notional border monitoring scenario that highlights the benefits of a network of counters compared to individual detectors, and (b) new insights into the Sequential Probability Ratio Test (SPRT) detection method, which lead to its adaptations for improved detection. Using KMB datasets from an outdoor test, we construct a notional border monitoring scenario, wherein twelve 2 *2 NaI detectors are deployed on the periphery of 21*21meter square region. A Cs-137 (175 uCi) source is moved across this region, starting several meters from outside and finally moving away. The measurements from individual counters and the network were processed using replays of a particle filter algorithm developed under IRSS program. The algorithm outputs from KMB datasets clearly illustrate the benefits of combining measurements from all networked counters: the source was detected before it entered the region, during its trajectory inside, and until it moved several meters away. When individual counters are used for detection, the source was detected for much shorter durations, and sometimes was missed in the interior region. The application of SPRT for detecting radiation sources requires choosing the detection threshold, which in turn requires a source strength

  6. Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm

    PubMed Central

    Parikh, Ankur P.; Curtis, Ross E.; Kuhn, Irene; Becker-Weimann, Sabine; Bissell, Mina; Xing, Eric P.; Wu, Wei

    2014-01-01

    The HMT3522 progression series of human breast cells have been used to discover how tissue architecture, microenvironment and signaling molecules affect breast cell growth and behaviors. However, much remains to be elucidated about malignant and phenotypic reversion behaviors of the HMT3522-T4-2 cells of this series. We employed a “pan-cell-state” strategy, and analyzed jointly microarray profiles obtained from different state-specific cell populations from this progression and reversion model of the breast cells using a tree-lineage multi-network inference algorithm, Treegl. We found that different breast cell states contain distinct gene networks. The network specific to non-malignant HMT3522-S1 cells is dominated by genes involved in normal processes, whereas the T4-2-specific network is enriched with cancer-related genes. The networks specific to various conditions of the reverted T4-2 cells are enriched with pathways suggestive of compensatory effects, consistent with clinical data showing patient resistance to anticancer drugs. We validated the findings using an external dataset, and showed that aberrant expression values of certain hubs in the identified networks are associated with poor clinical outcomes. Thus, analysis of various reversion conditions (including non-reverted) of HMT3522 cells using Treegl can be a good model system to study drug effects on breast cancer. PMID:25057922

  7. Software Defined Networking (SDN) controlled all optical switching networks with multi-dimensional switching architecture

    NASA Astrophysics Data System (ADS)

    Zhao, Yongli; Ji, Yuefeng; Zhang, Jie; Li, Hui; Xiong, Qianjin; Qiu, Shaofeng

    2014-08-01

    Ultrahigh throughout capacity requirement is challenging the current optical switching nodes with the fast development of data center networks. Pbit/s level all optical switching networks need to be deployed soon, which will cause the high complexity of node architecture. How to control the future network and node equipment together will become a new problem. An enhanced Software Defined Networking (eSDN) control architecture is proposed in the paper, which consists of Provider NOX (P-NOX) and Node NOX (N-NOX). With the cooperation of P-NOX and N-NOX, the flexible control of the entire network can be achieved. All optical switching network testbed has been experimentally demonstrated with efficient control of enhanced Software Defined Networking (eSDN). Pbit/s level all optical switching nodes in the testbed are implemented based on multi-dimensional switching architecture, i.e. multi-level and multi-planar. Due to the space and cost limitation, each optical switching node is only equipped with four input line boxes and four output line boxes respectively. Experimental results are given to verify the performance of our proposed control and switching architecture.

  8. A Simple Physical Optics Algorithm Perfect for Parallel Computing Architecture

    NASA Technical Reports Server (NTRS)

    Imbriale, W. A.; Cwik, T.

    1994-01-01

    A reflector antenna computer program based upon a simple discreet approximation of the radiation integral has proven to be extremely easy to adapt to the parallel computing architecture of the modest number of large-gain computing elements such as are used in the Intel iPSC and Touchstone Delta parallel machines.

  9. Cortical network architecture for context processing in primate brain

    PubMed Central

    Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka

    2015-01-01

    Context is information linked to a situation that can guide behavior. In the brain, context is encoded by sensory processing and can later be retrieved from memory. How context is communicated within the cortical network in sensory and mnemonic forms is unknown due to the lack of methods for high-resolution, brain-wide neuronal recording and analysis. Here, we report the comprehensive architecture of a cortical network for context processing. Using hemisphere-wide, high-density electrocorticography, we measured large-scale neuronal activity from monkeys observing videos of agents interacting in situations with different contexts. We extracted five context-related network structures including a bottom-up network during encoding and, seconds later, cue-dependent retrieval of the same network with the opposite top-down connectivity. These findings show that context is represented in the cortical network as distributed communication structures with dynamic information flows. This study provides a general methodology for recording and analyzing cortical network neuronal communication during cognition. DOI: http://dx.doi.org/10.7554/eLife.06121.001 PMID:26416139

  10. Training product unit neural networks with genetic algorithms

    NASA Technical Reports Server (NTRS)

    Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

    1991-01-01

    The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

  11. Recurrent neural networks training with stable bounding ellipsoid algorithm.

    PubMed

    Yu, Wen; de Jesús Rubio, José

    2009-06-01

    Bounding ellipsoid (BE) algorithms offer an attractive alternative to traditional training algorithms for neural networks, for example, backpropagation and least squares methods. The benefits include high computational efficiency and fast convergence speed. In this paper, we propose an ellipsoid propagation algorithm to train the weights of recurrent neural networks for nonlinear systems identification. Both hidden layers and output layers can be updated. The stability of the BE algorithm is proven.

  12. An open, interoperable, and scalable prehospital information technology network architecture.

    PubMed

    Landman, Adam B; Rokos, Ivan C; Burns, Kevin; Van Gelder, Carin M; Fisher, Roger M; Dunford, James V; Cone, David C; Bogucki, Sandy

    2011-01-01

    Some of the most intractable challenges in prehospital medicine include response time optimization, inefficiencies at the emergency medical services (EMS)-emergency department (ED) interface, and the ability to correlate field interventions with patient outcomes. Information technology (IT) can address these and other concerns by ensuring that system and patient information is received when and where it is needed, is fully integrated with prior and subsequent patient information, and is securely archived. Some EMS agencies have begun adopting information technologies, such as wireless transmission of 12-lead electrocardiograms, but few agencies have developed a comprehensive plan for management of their prehospital information and integration with other electronic medical records. This perspective article highlights the challenges and limitations of integrating IT elements without a strategic plan, and proposes an open, interoperable, and scalable prehospital information technology (PHIT) architecture. The two core components of this PHIT architecture are 1) routers with broadband network connectivity to share data between ambulance devices and EMS system information services and 2) an electronic patient care report to organize and archive all electronic prehospital data. To successfully implement this comprehensive PHIT architecture, data and technology requirements must be based on best available evidence, and the system must adhere to health data standards as well as privacy and security regulations. Recent federal legislation prioritizing health information technology may position federal agencies to help design and fund PHIT architectures.

  13. C-Mantec: a novel constructive neural network algorithm incorporating competition between neurons.

    PubMed

    Subirats, José L; Franco, Leonardo; Jerez, José M

    2012-02-01

    C-Mantec is a novel neural network constructive algorithm that combines competition between neurons with a stable modified perceptron learning rule. The neuron learning is governed by the thermal perceptron rule that ensures stability of the acquired knowledge while the architecture grows and while the neurons compete for new incoming information. Competition makes it possible that even after new units have been added to the network, existing neurons still can learn if the incoming information is similar to their stored knowledge, and this constitutes a major difference with existing constructing algorithms. The new algorithm is tested on two different sets of benchmark problems: a Boolean function set used in logic circuit design and a well studied set of real world problems. Both sets were used to analyze the size of the constructed architectures and the generalization ability obtained and to compare the results with those from other standard and well known classification algorithms. The problem of overfitting is also analyzed, and a new built-in method to avoid its effects is devised and successfully applied within an active learning paradigm that filter noisy examples. The results show that the new algorithm generates very compact neural architectures with state-of-the-art generalization capabilities.

  14. SDN architecture for optical packet and circuit integrated networks

    NASA Astrophysics Data System (ADS)

    Furukawa, Hideaki; Miyazawa, Takaya

    2016-02-01

    We have been developing an optical packet and circuit integrated (OPCI) network, which realizes dynamic optical path, high-density packet multiplexing, and flexible wavelength resource allocation. In the OPCI networks, a best-effort service and a QoS-guaranteed service are provided by employing optical packet switching (OPS) and optical circuit switching (OCS) respectively, and users can select these services. Different wavelength resources are assigned for OPS and OCS links, and the amount of their wavelength resources are dynamically changed in accordance with the service usage conditions. To apply OPCI networks into wide-area (core/metro) networks, we have developed an OPCI node with a distributed control mechanism. Moreover, our OPCI node works with a centralized control mechanism as well as a distributed one. It is therefore possible to realize SDN-based OPCI networks, where resource requests and a centralized configuration are carried out. In this paper, we show our SDN architecture for an OPS system that configures mapping tables between IP addresses and optical packet addresses and switching tables according to the requests from multiple users via a web interface. While OpenFlow-based centralized control protocol is coming into widespread use especially for single-administrative, small-area (LAN/data-center) networks. Here, we also show an interworking mechanism between OpenFlow-based networks (OFNs) and the OPCI network for constructing a wide-area network, and a control method of wavelength resource selection to automatically transfer diversified flows from OFNs to the OPCI network.

  15. Disrupted Brain Functional Network Architecture in Chronic Tinnitus Patients

    PubMed Central

    Chen, Yu-Chen; Feng, Yuan; Xu, Jin-Jing; Mao, Cun-Nan; Xia, Wenqing; Ren, Jun; Yin, Xindao

    2016-01-01

    Purpose: Resting-state functional magnetic resonance imaging (fMRI) studies have demonstrated the disruptions of multiple brain networks in tinnitus patients. Nonetheless, several studies found no differences in network processing between tinnitus patients and healthy controls (HCs). Its neural bases are poorly understood. To identify aberrant brain network architecture involved in chronic tinnitus, we compared the resting-state fMRI (rs-fMRI) patterns of tinnitus patients and HCs. Materials and Methods: Chronic tinnitus patients (n = 24) with normal hearing thresholds and age-, sex-, education- and hearing threshold-matched HCs (n = 22) participated in the current study and underwent the rs-fMRI scanning. We used degree centrality (DC) to investigate functional connectivity (FC) strength of the whole-brain network and Granger causality to analyze effective connectivity in order to explore directional aspects involved in tinnitus. Results: Compared to HCs, we found significantly increased network centrality in bilateral superior frontal gyrus (SFG). Unidirectionally, the left SFG revealed increased effective connectivity to the left middle orbitofrontal cortex (OFC), left posterior lobe of cerebellum (PLC), left postcentral gyrus, and right middle occipital gyrus (MOG) while the right SFG exhibited enhanced effective connectivity to the right supplementary motor area (SMA). In addition, the effective connectivity from the bilateral SFG to the OFC and SMA showed positive correlations with tinnitus distress. Conclusions: Rs-fMRI provides a new and novel method for identifying aberrant brain network architecture. Chronic tinnitus patients have disrupted FC strength and causal connectivity mostly in non-auditory regions, especially the prefrontal cortex (PFC). The current findings will provide a new perspective for understanding the neuropathophysiological mechanisms in chronic tinnitus. PMID:27458377

  16. Architecture and design of optical path networks utilizing waveband virtual links

    NASA Astrophysics Data System (ADS)

    Ito, Yusaku; Mori, Yojiro; Hasegawa, Hiroshi; Sato, Ken-ichi

    2016-02-01

    We propose a novel optical network architecture that uses waveband virtual links, each of which can carry several optical paths, to directly bridge distant node pairs. Future photonic networks should not only transparently cover extended areas but also expand fiber capacity. However, the traversal of many ROADM nodes impairs the optical signal due to spectrum narrowing. To suppress the degradation, the bandwidth of guard bands needs to be increased, which degrades fiber frequency utilization. Waveband granular switching allows us to apply broader pass-band filtering at ROADMs and to insert sufficient guard bands between wavebands with minimum frequency utilization offset. The scheme resolves the severe spectrum narrowing effect. Moreover, the guard band between optical channels in a waveband can be minimized, which increases the number of paths that can be accommodated per fiber. In the network, wavelength path granular routing is done without utilizing waveband virtual links, and it still suffers from spectrum narrowing. A novel network design algorithm that can bound the spectrum narrowing effect by limiting the number of hops (traversed nodes that need wavelength path level routing) is proposed in this paper. This algorithm dynamically changes the waveband virtual link configuration according to the traffic distribution variation, where optical paths that need many node hops are effectively carried by virtual links. Numerical experiments demonstrate that the number of necessary fibers is reduced by 23% compared with conventional optical path networks.

  17. PAC learning algorithms for functions approximated by feedforward networks

    SciTech Connect

    Rao, N.S.V.; Protopopescu, V.

    1996-06-01

    The authors present a class of efficient algorithms for PAC learning continuous functions and regressions that are approximated by feedforward networks. The algorithms are applicable to networks with unknown weights located only in the output layer and are obtained by utilizing the potential function methods of Aizerman et al. Conditions relating the sample sizes to the error bounds are derived using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can be directly adapted to concept learning problems.

  18. Strategies for concurrent processing of complex algorithms in data driven architectures

    NASA Technical Reports Server (NTRS)

    Stoughton, John W.; Mielke, Roland R.

    1988-01-01

    The purpose is to document research to develop strategies for concurrent processing of complex algorithms in data driven architectures. The problem domain consists of decision-free algorithms having large-grained, computationally complex primitive operations. Such are often found in signal processing and control applications. The anticipated multiprocessor environment is a data flow architecture containing between two and twenty computing elements. Each computing element is a processor having local program memory, and which communicates with a common global data memory. A new graph theoretic model called ATAMM which establishes rules for relating a decomposed algorithm to its execution in a data flow architecture is presented. The ATAMM model is used to determine strategies to achieve optimum time performance and to develop a system diagnostic software tool. In addition, preliminary work on a new multiprocessor operating system based on the ATAMM specifications is described.

  19. Finite-sample based learning algorithms for feedforward networks

    SciTech Connect

    Rao, N.S.V.; Protopopescu, V.; Mann, R.C.; Oblow, E.M.; Iyengar, S.S.

    1995-04-01

    We discuss two classes of convergent algorithms for learning continuous functions (and also regression functions) that are represented by FeedForward Networks (FFN). The first class of algorithms, applicable to networks with unknown weights located only in the output layer, is obtained by utilizing the potential function methods of Aizerman et al. The second class, applicable to general feedforward networks, is obtained by utilizing the classical Robbins-Monro style stochastic approximation methods. Conditions relating the sample sizes to the error bounds are derived for both classes of algorithms using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can also be directly applied to concept learning problems. A main distinguishing feature of the this work is that the sample sizes are based on explicit algorithms rather than information-based methods.

  20. Pathway-Dependent Effectiveness of Network Algorithms for Gene Prioritization

    PubMed Central

    Shim, Jung Eun; Hwang, Sohyun; Lee, Insuk

    2015-01-01

    A network-based approach has proven useful for the identification of novel genes associated with complex phenotypes, including human diseases. Because network-based gene prioritization algorithms are based on propagating information of known phenotype-associated genes through networks, the pathway structure of each phenotype might significantly affect the effectiveness of algorithms. We systematically compared two popular network algorithms with distinct mechanisms – direct neighborhood which propagates information to only direct network neighbors, and network diffusion which diffuses information throughout the entire network – in prioritization of genes for worm and human phenotypes. Previous studies reported that network diffusion generally outperforms direct neighborhood for human diseases. Although prioritization power is generally measured for all ranked genes, only the top candidates are significant for subsequent functional analysis. We found that high prioritizing power of a network algorithm for all genes cannot guarantee successful prioritization of top ranked candidates for a given phenotype. Indeed, the majority of the phenotypes that were more efficiently prioritized by network diffusion showed higher prioritizing power for top candidates by direct neighborhood. We also found that connectivity among pathway genes for each phenotype largely determines which network algorithm is more effective, suggesting that the network algorithm used for each phenotype should be chosen with consideration of pathway gene connectivity. PMID:26091506

  1. Automated Reconstruction Algorithm for Identification of 3D Architectures of Cribriform Ductal Carcinoma In Situ

    PubMed Central

    Norton, Kerri-Ann; Namazi, Sameera; Barnard, Nicola; Fujibayashi, Mariko; Bhanot, Gyan; Ganesan, Shridar; Iyatomi, Hitoshi; Ogawa, Koichi; Shinbrot, Troy

    2012-01-01

    Ductal carcinoma in situ (DCIS) is a pre-invasive carcinoma of the breast that exhibits several distinct morphologies but the link between morphology and patient outcome is not clear. We hypothesize that different mechanisms of growth may still result in similar 2D morphologies, which may look different in 3D. To elucidate the connection between growth and 3D morphology, we reconstruct the 3D architecture of cribriform DCIS from resected patient material. We produce a fully automated algorithm that aligns, segments, and reconstructs 3D architectures from microscopy images of 2D serial sections from human specimens. The alignment algorithm is based on normalized cross correlation, the segmentation algorithm uses histogram equilization, Otsu's thresholding, and morphology techniques to segment the duct and cribra. The reconstruction method combines these images in 3D. We show that two distinct 3D architectures are indeed found in samples whose 2D histological sections are similarly identified as cribriform DCIS. These differences in architecture support the hypothesis that luminal spaces may form due to different mechanisms, either isolated cell death or merging fronds, leading to the different architectures. We find that out of 15 samples, 6 were found to have ‘bubble-like’ cribra, 6 were found to have ‘tube-like’ criba and 3 were ‘unknown.’ We propose that the 3D architectures found, ‘bubbles’ and ‘tubes’, account for some of the heterogeneity of the disease and may be prognostic indicators of different patient outcomes. PMID:22970156

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

    NASA Astrophysics Data System (ADS)

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

    2011-06-01

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

  3. Cross-fertilization between connectionist networks and highly parallel architectures

    NASA Technical Reports Server (NTRS)

    Barnden, John; Srinivas, Kankanahalli

    1989-01-01

    The theoretical and practical connections between connectionist schemes such as neural-network computers and traditional symbolic processing architectures involving a high degree of parallelism are explored, reviewing the results of recent investigations. Topics addressed include data flow, data structure, and control flow; conventional pointers; associative addressing; hashing and reduced representations; the problem of binding values to variables; and levels of parallelism. It is concluded that connectionism is more closely related to traditional computer science and technology than is generally admitted; more cooperation between followers of the two approaches is recommended.

  4. A Modular Ring Architecture for Large Scale Neural Network Implementations

    NASA Astrophysics Data System (ADS)

    Jump, Lance B.; Ligomenides, Panos A.

    1989-11-01

    Constructing fully parallel, large scale, neural networks is complicated by the problems of providing for massive interconnectivity and of overcoming fan in/out limitations in area-efficient VLSI/WSI realizations. A modular, bus switched, neural ring architecture employing primitive ring (pRing) processors is proposed, which solves the fan in/out and connectivity problems by a dynamically reconfigurable communication ring that synchronously serves identical, radially connected, processing elements. It also allows cost versus performance trade-offs by the assignment of variable numbers of logical neurons to each physical processing element.

  5. Algorithm To Architecture Mapping Model (ATAMM) multicomputer operating system functional specification

    NASA Technical Reports Server (NTRS)

    Mielke, R.; Stoughton, J.; Som, S.; Obando, R.; Malekpour, M.; Mandala, B.

    1990-01-01

    A functional description of the ATAMM Multicomputer Operating System is presented. ATAMM (Algorithm to Architecture Mapping Model) is a marked graph model which describes the implementation of large grained, decomposed algorithms on data flow architectures. AMOS, the ATAMM Multicomputer Operating System, is an operating system which implements the ATAMM rules. A first generation version of AMOS which was developed for the Advanced Development Module (ADM) is described. A second generation version of AMOS being developed for the Generic VHSIC Spaceborne Computer (GVSC) is also presented.

  6. A hybrid 802.16/802.11 network architecture for a United States coastal area network

    NASA Astrophysics Data System (ADS)

    Burbank, Jack L.; Kasch, William T.; Andrusenko, Julia; Haberman, Brian K.; Nichols, Robert; Zheng, Harold

    2007-04-01

    This paper presents a concept for a United States Coastal Area Network (U-SCAN) that is comprised of IEEE 802.11, 802.16, and satellite communications technologies. The Office of Naval Research (ONR) on behalf of the National Oceanographic Partnership Program (NOPP) has tasked The Johns Hopkins University Applied Physics Laboratory (JHU/APL) to perform an architectural study into the establishment of a United States Coastal Area Network (U-SCAN). The goal of this study is to define a wireless network architecture that can be deployed to enable contiguous coastal area network coverage for scientific, commercial, and homeland security (e.g. Coast Guard) applications within the United States Exclusive Economic Zone (EEZ), in a manner that is flexible, manageable, and affordable. The JHU/APL study will ultimately provide recommendations to NOPP regarding potential network architectures and technologies that could provide the desired capability, with a particular focus on commercial (both existing and emerging) technologies. This paper presents the envisioned U-SCAN architecture, and presents the envisioned technical capabilities and shortcomings of the component candidate technologies.

  7. SANDS: An Architecture for Clinical Decision Support in a National Health Information Network

    PubMed Central

    Wright, Adam; Sittig, Dean F.

    2007-01-01

    A new architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support) is introduced and its performance evaluated. The architecture provides a method for performing clinical decision support across a network, as in a health information exchange. Using the prototype we demonstrated that, first, a number of useful types of decision support can be carried out using our architecture; and, second, that the architecture exhibits desirable reliability and performance characteristics. PMID:18693950

  8. Network design sensitivity studies for use of digital cross-connect systems in survivable network architectures

    NASA Astrophysics Data System (ADS)

    Doverspike, Robert D.; Morgan, Jonathan A.; Leland, Will

    1994-01-01

    This paper provides the results of an economic study on the use of SONET Digital Cross-connect Systems (DCS's) to provide survivable transmission network architectures in local exchange networks. Three fundamental survivable transmission technologies are considered: (1) a SONET self-healing ring; (2) a SONET point-to-point fiber system with 1:1 automatic protection switching and diverse routing of protection facilities; and (3) a DCS mesh with automatic DCS restoration (rerouting) protection. These three technologies are used in various combinations to form six survivable network alternatives for evaluation. Two Local Exchange Carrier (LEC) networks are used (a 15 node network and a 53 node network) and demand, network connectivity, and unit equipment cost sensitivities are evaluated on these alternatives. In addition, the survivability of each alternative in the event of major node failure is calculated. The motivation for the study is to determine the viability of DCS-based survivable network architectures and, in particular, the viability of SONET DCS's with integrated optical terminations. The study has two objectives: (1) given a specific survivable network technology, under what conditions is it economical to place a Broadband DCS (B-DCS) in a central office as opposed Add-Drop Multiplexers (ADM's); and (2) which survivable technologies with B-DCS's are economical, and under networks consists of 'hybrids' of SONET point-to-point, ring and mesh technologies, and that the B-DCS is economically viable for interconnection between these technologies.

  9. Implementation of an efficient labeling algorithm on a pipelined architecture

    NASA Astrophysics Data System (ADS)

    Olsson, Olof J.; Penman, David W.

    1992-11-01

    This paper describes an efficient approach, developed by the authors, for labelling images using a combination of pipeline (Datacube) and host (general purpose computer) processing. The output of the algorithm is a coordinate list of labelled object pixels that facilitates further high level operations.

  10. Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

    NASA Technical Reports Server (NTRS)

    Kobayashi, Takahisa; Simon, Donald L.

    2002-01-01

    As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

  11. Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network

    NASA Technical Reports Server (NTRS)

    Rajkumar, T.; Aragon, Cecilia; Bardina, Jorge; Britten, Roy

    2002-01-01

    A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.

  12. On-board processing satellite network architecture and control study

    NASA Technical Reports Server (NTRS)

    Campanella, S. Joseph; Pontano, B.; Chalmers, H.

    1987-01-01

    For satellites to remain a vital part of future national and international communications, system concepts that use their inherent advantages to the fullest must be created. Network architectures that take maximum advantage of satellites equipped with onboard processing are explored. Satellite generations must accommodate various services for which satellites constitute the preferred vehicle of delivery. Such services tend to be those that are widely dispersed and present thin to medium loads to the system. Typical systems considered are thin and medium route telephony, maritime, land and aeronautical radio, VSAT data, low bit rate video teleconferencing, and high bit rate broadcast of high definition video. Delivery of services by TDMA and FDMA multiplexing techniques and combinations of the two for individual and mixed service types are studied. The possibilities offered by onboard circuit switched and packet switched architectures are examined and the results strongly support a preference for the latter. A detailed design architecture encompassing the onboard packet switch and its control, the related demand assigned TDMA burst structures, and destination packet protocols for routing traffic are presented. Fundamental onboard hardware requirements comprising speed, memory size, chip count, and power are estimated. The study concludes with identification of key enabling technologies and identifies a plan to develop a POC model.

  13. Architectural approach for quality and safety aware healthcare social networks.

    PubMed

    López, Diego M; Blobel, Bernd; González, Carolina

    2012-01-01

    Quality of information and privacy and safety issues are frequently identified as main limitations to make most benefit from social media in healthcare. The objective of the paper is to contribute to the analysis of healthcare social networks (SN), and online healthcare social network services (SNS) by proposing a formal architectural analysis of healthcare SN and SNS, considering the complexity of both systems, but stressing on quality, safety and usability aspects. Quality policies are necessary to control the quality of content published by experts and consumers. Privacy and safety policies protect against inappropriate use of information and users responsibility for sharing information. After the policies are established and documented, a proof of concept online SNS supporting primary healthcare promotion is presented in the paper.

  14. An architecture for designing fuzzy logic controllers using neural networks

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1991-01-01

    Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.

  15. Design principles of regulatory networks: searching for the molecular algorithms of the cell.

    PubMed

    Lim, Wendell A; Lee, Connie M; Tang, Chao

    2013-01-24

    A challenge in biology is to understand how complex molecular networks in the cell execute sophisticated regulatory functions. Here we explore the idea that there are common and general principles that link network structures to biological functions, principles that constrain the design solutions that evolution can converge upon for accomplishing a given cellular task. We describe approaches for classifying networks based on abstract architectures and functions, rather than on the specific molecular components of the networks. For any common regulatory task, can we define the space of all possible molecular solutions? Such inverse approaches might ultimately allow the assembly of a design table of core molecular algorithms that could serve as a guide for building synthetic networks and modulating disease networks.

  16. Design Principles of Regulatory Networks: Searching for the Molecular Algorithms of the Cell

    PubMed Central

    Lim, Wendell A.; Lee, Connie M.; Tang, Chao

    2013-01-01

    A challenge in biology is to understand how complex molecular networks in the cell execute sophisticated regulatory functions. Here we explore the idea that there are common and general principles that link network structures to biological functions, principles that constrain the design solutions that evolution can converge upon for accomplishing a given cellular task. We describe approaches for classifying networks based on abstract architectures and functions, rather than on the specific molecular components of the networks. For any common regulatory task, can we define the space of all possible molecular solutions? Such inverse approaches might ultimately allow the assembly of a design table of core molecular algorithms that could serve as a guide for building synthetic networks and modulating disease networks. PMID:23352241

  17. Wireless Communications. Wireless Network Integration Technology: MIRAI Architecture for Heterogeneous Network

    NASA Astrophysics Data System (ADS)

    Mizuno, Mitsuhiko; Wu, Gang; Havinga, Paul J. M.

    2001-12-01

    One of the keywords that describe next generation wireless communications is "seamless." As part of the e-Japan Plan promoted by the Japanese Government, the MIRAI (Multimedia Integrated network by Radio Access Innovation) project has, as its goal, the development of new technologies to enable seamless integration of various wireless access systems for practical use by the year 2005. This paper describes a heterogeneous network architecture including a common tool, a common platform, and a common access. In particular, software-defined radio technologies are used to develop a multi-service user terminal to access different wireless networks. The common platform for various wireless networks is based on a wireless supporting IPv6 network. A basic access network, separated from other wireless access networks, is used as a means for wireless system discovery, signaling, and paging. A proof-concept experimental demonstration system will be available in March, 2002.

  18. A high throughput architecture for a low complexity soft-output demapping algorithm

    NASA Astrophysics Data System (ADS)

    Ali, I.; Wasenmüller, U.; Wehn, N.

    2015-11-01

    Iterative channel decoders such as Turbo-Code and LDPC decoders show exceptional performance and therefore they are a part of many wireless communication receivers nowadays. These decoders require a soft input, i.e., the logarithmic likelihood ratio (LLR) of the received bits with a typical quantization of 4 to 6 bits. For computing the LLR values from a received complex symbol, a soft demapper is employed in the receiver. The implementation cost of traditional soft-output demapping methods is relatively large in high order modulation systems, and therefore low complexity demapping algorithms are indispensable in low power receivers. In the presence of multiple wireless communication standards where each standard defines multiple modulation schemes, there is a need to have an efficient demapper architecture covering all the flexibility requirements of these standards. Another challenge associated with hardware implementation of the demapper is to achieve a very high throughput in double iterative systems, for instance, MIMO and Code-Aided Synchronization. In this paper, we present a comprehensive communication and hardware performance evaluation of low complexity soft-output demapping algorithms to select the best algorithm for implementation. The main goal of this work is to design a high throughput, flexible, and area efficient architecture. We describe architectures to execute the investigated algorithms. We implement these architectures on a FPGA device to evaluate their hardware performance. The work has resulted in a hardware architecture based on the figured out best low complexity algorithm delivering a high throughput of 166 Msymbols/second for Gray mapped 16-QAM modulation on Virtex-5. This efficient architecture occupies only 127 slice registers, 248 slice LUTs and 2 DSP48Es.

  19. Space Mobile Network: A Near Earth Communications and Navigation Architecture

    NASA Technical Reports Server (NTRS)

    Israel, David J.; Heckler, Gregory W.; Menrad, Robert J.

    2016-01-01

    This paper shares key findings of NASA's Earth Regime Network Evolution Study (ERNESt) team resulting from its 18-month effort to define a wholly new architecture-level paradigm for the exploitation of space by civil space and commercial sector organizations. Since the launch of Sputnik in October 1957 spaceflight missions have remained highly scripted activities from launch through disposal. The utilization of computer technology has enabled dramatic increases in mission complexity; but, the underlying premise that the diverse actions necessary to meet mission goals requires minute-by-minute scripting, defined weeks in advance of execution, for the life of the mission has remained. This archetype was appropriate for a "new frontier" but now risks overtly constraining the potential market-based opportunities for the innovation considered necessary to efficiently address the complexities associated with meeting communications and navigation requirements projected to be characteristics of the next era of space exploration: a growing number of missions in simultaneous execution, increased variance of mission types and growth in location/orbital regime diversity. The resulting ERNESt architectural cornerstone - the Space Mobile Network (SMN) - was envisioned as critical to creating an environment essential to meeting these future challenges in political, programmatic, technological and budgetary terms. The SMN incorporates technologies such as: Disruption Tolerant Networking (DTN) and optical communications, as well as new operations concepts such as User Initiated Services (UIS) to provide user services analogous to today's terrestrial mobile network user. Results developed in collaboration with NASA's Space Communications and Navigation (SCaN) Division and field centers are reported on. Findings have been validated via briefings to external focus groups and initial ground-based demonstrations. The SMN opens new niches for exploitation by the marketplace of mission

  20. Multifractal analysis of weighted networks by a modified sandbox algorithm

    PubMed Central

    Song, Yu-Qin; Liu, Jin-Long; Yu, Zu-Guo; Li, Bao-Gen

    2015-01-01

    Complex networks have attracted growing attention in many fields. As a generalization of fractal analysis, multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. Some algorithms for MFA of unweighted complex networks have been proposed in the past a few years, including the sandbox (SB) algorithm recently employed by our group. In this paper, a modified SB algorithm (we call it SBw algorithm) is proposed for MFA of weighted networks. First, we use the SBw algorithm to study the multifractal property of two families of weighted fractal networks (WFNs): “Sierpinski” WFNs and “Cantor dust” WFNs. We also discuss how the fractal dimension and generalized fractal dimensions change with the edge-weights of the WFN. From the comparison between the theoretical and numerical fractal dimensions of these networks, we can find that the proposed SBw algorithm is efficient and feasible for MFA of weighted networks. Then, we apply the SBw algorithm to study multifractal properties of some real weighted networks — collaboration networks. It is found that the multifractality exists in these weighted networks, and is affected by their edge-weights. PMID:26634304

  1. Multifractal analysis of weighted networks by a modified sandbox algorithm

    NASA Astrophysics Data System (ADS)

    Song, Yu-Qin; Liu, Jin-Long; Yu, Zu-Guo; Li, Bao-Gen

    2015-12-01

    Complex networks have attracted growing attention in many fields. As a generalization of fractal analysis, multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. Some algorithms for MFA of unweighted complex networks have been proposed in the past a few years, including the sandbox (SB) algorithm recently employed by our group. In this paper, a modified SB algorithm (we call it SBw algorithm) is proposed for MFA of weighted networks. First, we use the SBw algorithm to study the multifractal property of two families of weighted fractal networks (WFNs): “Sierpinski” WFNs and “Cantor dust” WFNs. We also discuss how the fractal dimension and generalized fractal dimensions change with the edge-weights of the WFN. From the comparison between the theoretical and numerical fractal dimensions of these networks, we can find that the proposed SBw algorithm is efficient and feasible for MFA of weighted networks. Then, we apply the SBw algorithm to study multifractal properties of some real weighted networks — collaboration networks. It is found that the multifractality exists in these weighted networks, and is affected by their edge-weights.

  2. Architecture and methods for UAV-based heterogeneous sensor network applications

    NASA Astrophysics Data System (ADS)

    Antonio, Pedro; Caputo, Davide; Gandelli, Alessandro; Grimaccia, Francesco; Mussetta, Marco

    2012-09-01

    Wireless sensor netwoks (WSN) employ miniaturized devices which integrate sensing, processing, and communication capabilities. In this paper an innovative mobile platform for heterogeneous sensor networks is presented, combined with adaptive methods to optimize the communication architecture for novel potential applications even in coastal and marine environment monitoring. In fact, in the near future, WSN data collection could be performed by UAV platforms which can be a sink for ground sensors layer, acting essentially as a mobile gateway. In order to maximize the system performances and the network lifespan, the authors propose a recently developed hybrid technique based on evolutionary algorithms. This procedure is here applied to optimize the communication energy consumption in WSN by selecting the optimal multi-hop routing schemes, with a suitable hybridization of different routing criteria. The proposed approach can be potentially extended and applied to ongoing research projects focused on UAV-based remote sensing of the ocean, sea ice, coastal waters, and large water regions.

  3. Architecture and Design of IP Broadcasting System Using Passive Optical Network

    NASA Astrophysics Data System (ADS)

    Ikeda, Hiroki; Sugawa, Jun; Ashi, Yoshihiro; Sakamoto, Kenichi

    We propose an IP broadcasting system architecture using passive optical networks (PON) utilizing the optical broadcast links of a PON with a downstream bandwidth allocation algorithm to provide a multi-channel IP broadcasting service to home subscribers on single broadband IP network infrastructures. We introduce the design and adaptation of the optical broadcast links to effectively broadcast video contents to home subscribers. We present a performance analysis that includes the downstream bandwidth utilization efficiency of the broadcast link and the bandwidth control of the IP broadcasting and Internet data. Our analysis and simulation results show that the proposed system can provide 100 HDTV channels to every user over fiber lines. We also propose an IPTV channel selection mechanism in an ONT by selecting a broadcast stream. We developed and evaluated a prototype that can achieve a 15-msec IPTV channel selection speed.

  4. A utility oriented radio resource management algorithm for heterogenous network

    NASA Astrophysics Data System (ADS)

    Wang, Xiaoyan; Dong, Yan; Huang, Zailu

    2007-11-01

    A utility oriented radio resource management algorithm is proposed for broadband nongeostationary satellite network which works in the heterogeneous network environment and provides access services for various customers on the ground. Based on the game theory, the problem for optimizing the network's performance is turned into the problem for maximizing the network's long term utility in the proposed algorithm. With evaluation to the traffic condition and dimensions of Qos for the network at the moment while the access service requirements changing, the influence of this service requirement to the long term utility of the satellite network is audited and then the resource assignment decision can be made according to the rule for maximizing the satellite network's long term utility. The process directed by game theory guaranteed both that the benefit of the network and the requirements of the customers could be considered synthetically. The simulation results demonstrate the effectiveness of the proposed algorithm.

  5. A Network Selection Algorithm Considering Power Consumption in Hybrid Wireless Networks

    NASA Astrophysics Data System (ADS)

    Joe, Inwhee; Kim, Won-Tae; Hong, Seokjoon

    In this paper, we propose a novel network selection algorithm considering power consumption in hybrid wireless networks for vertical handover. CDMA, WiBro, WLAN networks are candidate networks for this selection algorithm. This algorithm is composed of the power consumption prediction algorithm and the final network selection algorithm. The power consumption prediction algorithm estimates the expected lifetime of the mobile station based on the current battery level, traffic class and power consumption for each network interface card of the mobile station. If the expected lifetime of the mobile station in a certain network is not long enough compared the handover delay, this particular network will be removed from the candidate network list, thereby preventing unnecessary handovers in the preprocessing procedure. On the other hand, the final network selection algorithm consists of AHP (Analytic Hierarchical Process) and GRA (Grey Relational Analysis). The global factors of the network selection structure are QoS, cost and lifetime. If user preference is lifetime, our selection algorithm selects the network that offers longest service duration due to low power consumption. Also, we conduct some simulations using the OPNET simulation tool. The simulation results show that the proposed algorithm provides longer lifetime in the hybrid wireless network environment.

  6. Engineered skeletal muscle tissue networks with controllable architecture

    PubMed Central

    Bian, Weining; Bursac, Nenad

    2009-01-01

    The engineering of functional skeletal muscle tissue substitutes holds promise for the treatment of various muscular diseases and injuries. However, no tissue fabrication technology currently exists for the generation of a relatively large and thick bioartificial muscle made of densely packed, uniformly aligned, and differentiated myofibers. In this study, we describe a versatile cell/hydrogel micromolding approach where polydimethylsiloxane (PDMS) molds containing an array of elongated posts were used to fabricate relatively large neonatal rat skeletal muscle tissue networks with reproducible and controllable architecture. By combining cell-mediated fibrin gel compaction and precise microfabrication of mold dimensions including the length and height of the PDMS posts, we were able to simultaneously support high cell viability, guide cell alignment along the microfabricated tissue pores, and reproducibly control the overall tissue porosity, size, and thickness. The interconnected muscle bundles within the porous tissue networks were composed of densely packed, aligned, and highly differentiated myofibers. The formed myofibers expressed myogenin, developed abundant cross-striations, and generated spontaneous tissue contractions at the macroscopic spatial scale. The proliferation of non-muscle cells was significantly reduced compared to monolayer cultures. The more complex muscle tissue architectures were fabricated by controlling the spatial distribution and direction of the PDMS posts. PMID:19070360

  7. Vehicle dynamic analysis using neuronal network algorithms

    NASA Astrophysics Data System (ADS)

    Oloeriu, Florin; Mocian, Oana

    2014-06-01

    being a non-linear process identification algorithm. The common use of neuronal networks for non-linear processes is justified by the fact that both have the ability to organize by themselves. That is why the neuronal networks best define intelligent systems, thus the word `neuronal' is sending one's mind to the biological neuron cell. The paper presents how to better interpret data fed from the on-board computer and a new way of processing that data to better model the real life dynamic behavior of the vehicle.

  8. Learning algorithms for feedforward networks based on finite samples

    SciTech Connect

    Rao, N.S.V.; Protopopescu, V.; Mann, R.C.; Oblow, E.M.; Iyengar, S.S.

    1994-09-01

    Two classes of convergent algorithms for learning continuous functions (and also regression functions) that are represented by feedforward networks, are discussed. The first class of algorithms, applicable to networks with unknown weights located only in the output layer, is obtained by utilizing the potential function methods of Aizerman et al. The second class, applicable to general feedforward networks, is obtained by utilizing the classical Robbins-Monro style stochastic approximation methods. Conditions relating the sample sizes to the error bounds are derived for both classes of algorithms using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can be directly adapted to concept learning problems.

  9. A layer reduction based community detection algorithm on multiplex networks

    NASA Astrophysics Data System (ADS)

    Wang, Xiaodong; Liu, Jing

    2017-04-01

    Detecting hidden communities is important for the analysis of complex networks. However, many algorithms have been designed for single layer networks (SLNs) while just a few approaches have been designed for multiplex networks (MNs). In this paper, we propose an algorithm based on layer reduction for detecting communities on MNs, which is termed as LRCD-MNs. First, we improve a layer reduction algorithm termed as neighaggre to combine similar layers and keep others separated. Then, we use neighaggre to find the community structure hidden in MNs. Experiments on real-life networks show that neighaggre can obtain higher relative entropy than the other algorithm. Moreover, we apply LRCD-MNs on some real-life and synthetic multiplex networks and the results demonstrate that, although LRCD-MNs does not have the advantage in terms of modularity, it can obtain higher values of surprise, which is used to evaluate the quality of partitions of a network.

  10. Firewall Architectures for High-Speed Networks: Final Report

    SciTech Connect

    Errin W. Fulp

    2007-08-20

    Firewalls are a key component for securing networks that are vital to government agencies and private industry. They enforce a security policy by inspecting and filtering traffic arriving or departing from a secure network. While performing these critical security operations, firewalls must act transparent to legitimate users, with little or no effect on the perceived network performance (QoS). Packets must be inspected and compared against increasingly complex rule sets and tables, which is a time-consuming process. As a result, current firewall systems can introduce significant delays and are unable to maintain QoS guarantees. Furthermore, firewalls are susceptible to Denial of Service (DoS) attacks that merely overload/saturate the firewall with illegitimate traffic. Current firewall technology only offers a short-term solution that is not scalable; therefore, the \\textbf{objective of this DOE project was to develop new firewall optimization techniques and architectures} that meet these important challenges. Firewall optimization concerns decreasing the number of comparisons required per packet, which reduces processing time and delay. This is done by reorganizing policy rules via special sorting techniques that maintain the original policy integrity. This research is important since it applies to current and future firewall systems. Another method for increasing firewall performance is with new firewall designs. The architectures under investigation consist of multiple firewalls that collectively enforce a security policy. Our innovative distributed systems quickly divide traffic across different levels based on perceived threat, allowing traffic to be processed in parallel (beyond current firewall sandwich technology). Traffic deemed safe is transmitted to the secure network, while remaining traffic is forwarded to lower levels for further examination. The result of this divide-and-conquer strategy is lower delays for legitimate traffic, higher throughput

  11. Computer Algorithms and Architectures for Three-Dimensional Eddy-Current Nondestructive Evaluation. Volume 1. Executive Summary

    DTIC Science & Technology

    1989-01-20

    LLAA6 .l iI -SA/TR-2/89 A003: FINAL REPORT * COMPUTER ALGORITHMS AND ARCHITECTURES N FOR THREE-DIMENSIONAL EDDY-CURRENT NONDESTRUCTIVE EVALUATION...Ciasuication) COMPUTER ALGORITHMS AND ARCHITECTURES FOR THREE-DIMENSIONAL EDD~j~~JRRN iv ummary Q PERSONAL AUTriOR(S) SBAHASCAE 1 3a. TYPE Of REPORT

  12. Proposal of a multi-layer network architecture for OBS/GMPLS network interworking

    NASA Astrophysics Data System (ADS)

    Guo, Hongxiang; Tsuritani, Takehiro; Yin, Yawei; Otani, Tomohiro; Wu, Jian

    2007-11-01

    In order to enable the existing optical circuit switching (OCS) network to support both wavelength and subwavelength granularities, this paper proposes overlay-based multi-layer network architecture for interworking the generalized multi-protocol label switching (GMPLS) controlled OCS network with optical burst switching (OBS) networks. A dedicated GMPLS border controller with necessary GMPLS extensions, including group label switching path (LSP) provisioning, node capability advertisement, and standard wavelength label as well as wavelength availability advertisement, is introduced in this multi-layer network to enable a simple but flexible interworking operation. The feasibility of this proposal is experimentally confirmed by demonstrating an OBS/GMPLS testbed, in which the extended node capability advertisement and group LSP functions successfully enabled the burst header packet (BHP) and data burst (DB) to transmit over a GMPLS-controlled transparent OCS network.

  13. On the use of harmony search algorithm in the training of wavelet neural networks

    NASA Astrophysics Data System (ADS)

    Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

    2015-10-01

    Wavelet neural networks (WNNs) are a class of feedforward neural networks that have been used in a wide range of industrial and engineering applications to model the complex relationships between the given inputs and outputs. The training of WNNs involves the configuration of the weight values between neurons. The backpropagation training algorithm, which is a gradient-descent method, can be used for this training purpose. Nonetheless, the solutions found by this algorithm often get trapped at local minima. In this paper, a harmony search-based algorithm is proposed for the training of WNNs. The training of WNNs, thus can be formulated as a continuous optimization problem, where the objective is to maximize the overall classification accuracy. Each candidate solution proposed by the harmony search algorithm represents a specific WNN architecture. In order to speed up the training process, the solution space is divided into disjoint partitions during the random initialization step of harmony search algorithm. The proposed training algorithm is tested onthree benchmark problems from the UCI machine learning repository, as well as one real life application, namely, the classification of electroencephalography signals in the task of epileptic seizure detection. The results obtained show that the proposed algorithm outperforms the traditional harmony search algorithm in terms of overall classification accuracy.

  14. Tensor Network Algorithms for Braiding Anyons

    NASA Astrophysics Data System (ADS)

    Ayeni, Babatunde; Singh, Sukhwinder; Pfeifer, Robert; Brennen, Gavin

    Anyons are point-like (quasi)particles which exist only in two-dimensional systems and have exchange statistics that are neither bosonic nor fermionic. These particles were first proposed as a mere theoretical curiosity, but it was later shown that they arise in topological states of matter and that certain species of non-Abelian anyons can be used for low error quantum computation. Despite the importance of anyons, fundamentally and technologically, comparatively little is understood about their many body behaviour especially when the non local effects of braiding are taken into account. This largely due to the lack of efficient numerical methods to study them. In order to circumvent this problem, and to broaden our understanding of the physics of anyons, the authors have developed several numerical methods based on tensor network algorithms including: anyonic Matrix Product States (MPS), anyonic Time Evolving Block Decimation (TEBD), anyonic Density Matrix Renormalization Group (DMRG), and Anyonic U(1) MPS. These can be used to simulate static interacting and itinerant braiding anyons on a finite or infinite lattice. We have used our methods to study the phase diagrams of some species, such as Abelian Z3 anyons and non-Abelian Fibonacci and Ising.

  15. An Open Distributed Architecture for Sensor Networks for Risk Management

    PubMed Central

    Douglas, John; Usländer, Thomas; Schimak, Gerald; Esteban, J. Fernando; Denzer, Ralf

    2008-01-01

    Sensors provide some of the basic input data for risk management of natural and man-made hazards. Here the word ‘sensors’ covers everything from remote sensing satellites, providing invaluable images of large regions, through instruments installed on the Earth's surface to instruments situated in deep boreholes and on the sea floor, providing highly-detailed point-based information from single sites. Data from such sensors is used in all stages of risk management, from hazard, vulnerability and risk assessment in the pre-event phase, information to provide on-site help during the crisis phase through to data to aid in recovery following an event. Because data from sensors play such an important part in improving understanding of the causes of risk and consequently in its mitigation, considerable investment has been made in the construction and maintenance of highly-sophisticated sensor networks. In spite of the ubiquitous need for information from sensor networks, the use of such data is hampered in many ways. Firstly, information about the presence and capabilities of sensor networks operating in a region is difficult to obtain due to a lack of easily available and usable meta-information. Secondly, once sensor networks have been identified their data it is often difficult to access due to a lack of interoperability between dissemination and acquisition systems. Thirdly, the transfer and processing of information from sensors is limited, again by incompatibilities between systems. Therefore, the current situation leads to a lack of efficiency and limited use of the available data that has an important role to play in risk mitigation. In view of this situation, the European Commission (EC) is funding a number of Integrated Projects within the Sixth Framework Programme concerned with improving the accessibility of data and services for risk management. Two of these projects: ‘Open Architecture and Spatial Data Infrastructure for Risk Management’ (ORCHESTRA

  16. Analysis of Community Detection Algorithms for Large Scale Cyber Networks

    SciTech Connect

    Mane, Prachita; Shanbhag, Sunanda; Kamath, Tanmayee; Mackey, Patrick S.; Springer, John

    2016-09-30

    The aim of this project is to use existing community detection algorithms on an IP network dataset to create supernodes within the network. This study compares the performance of different algorithms on the network in terms of running time. The paper begins with an introduction to the concept of clustering and community detection followed by the research question that the team aimed to address. Further the paper describes the graph metrics that were considered in order to shortlist algorithms followed by a brief explanation of each algorithm with respect to the graph metric on which it is based. The next section in the paper describes the methodology used by the team in order to run the algorithms and determine which algorithm is most efficient with respect to running time. Finally, the last section of the paper includes the results obtained by the team and a conclusion based on those results as well as future work.

  17. Architecture of the Florida power grid as a complex network

    NASA Astrophysics Data System (ADS)

    Xu, Yan; Gurfinkel, Aleks Jacob; Rikvold, Per Arne

    2014-05-01

    We study the Florida high-voltage power grid as a technological network embedded in space. Measurements of geographical lengths of transmission lines, the mixing of generators and loads, the weighted clustering coefficient, as well as the organization of edge conductance weights show a complex architecture quite different from random-graph models usually considered. In particular, we introduce a parametrized mixing matrix to characterize the mixing pattern of generators and loads in the Florida Grid, which is intermediate between the random mixing case and the semi-bipartite case where generator-generator transmission lines are forbidden. Our observations motivate an investigation of optimization (design) principles leading to the structural organization of power grids. We thus propose two network optimization models for the Florida Grid as a case study. Our results show that the Florida Grid is optimized not only by reducing the construction cost (measured by the total length of power lines), but also through reducing the total pairwise edge resistance in the grid, which increases the robustness of power transmission between generators and loads against random line failures. We then embed our models in spatial areas of different aspect ratios and study how this geometric factor affects the network structure, as well as the box-counting fractal dimension of the grids generated by our models.

  18. An architecture for distributed video applications based on declarative networking

    NASA Astrophysics Data System (ADS)

    Wang, Xiping; Gonzales, Cesar; Lobo, Jorge; Calo, Seraphin; Verma, Dinesh

    2012-06-01

    Video surveillance applications are examples of complex distributed coalition tasks. Real-time capture and analysis of image sensor data is one of the most important tasks in a number of military critical decision making scenarios. In complex battlefield situations, there is a need to coordinate the operation of distributed image sensors and the analysis of their data as transmitted over a heterogeneous wireless network where bandwidth, power, and computational capabilities are constrained. There is also a need to automate decision making based on the results of the analysis of video data. Declarative Networking is a promising technology for controlling complex video surveillance applications in this sort of environment. This paper presents a flexible and extensible architecture for deploying distributed video surveillance applications using the declarative networking paradigm, which allows us to dynamically connect and manage distributed image sensors and deploy various modules for the analysis of video data to satisfy a variety of video surveillance requirements. With declarative computing, it becomes possible for us not only to express the program control structure in a declarative fashion, but also to simplify the management of distributed video surveillance applications.

  19. Architecture of the Florida Power Grid as a Complex Network

    NASA Astrophysics Data System (ADS)

    Xu, Yan; Gurfinkel, Aleks Jacob; Rikvold, Per Arne

    2014-03-01

    Power grids are the largest engineered systems ever built. Our work presents a simple and self-consistent graph-theoretic analysis of the Florida high-voltage power grid as a technological network embedded in two-dimensional space. We take a new perspective on the mixing patterns of generators and loads in power grids, pointing out that the real grid is usually intermediate between the random mixing and semi-bipartite case (in which generator-generator power transmission lines are disallowed). We propose spatial network models for power grids, which are obtained via a Monte Carlo cooling optimization process. Our results suggest some possible design principles behind the complex architecture of the Florida grid, viz. balancing low construction cost (measured by the total length of transmission lines) and an indispensable redundancy (measured by the clustering coefficient and edge multiplicity) responsible for the robustness of the grid. We also study community structures (modularity) of the real and modeled power-grid networks. Such communities can be electrically separated from each other to limit cascading power failures, a technique known as intentional islanding. Supported by NSF Grant No. DMR-1104829.

  20. Service Oriented Architecture for Wireless Sensor Networks in Agriculture

    NASA Astrophysics Data System (ADS)

    Sawant, S. A.; Adinarayana, J.; Durbha, S. S.; Tripathy, A. K.; Sudharsan, D.

    2012-08-01

    Rapid advances in Wireless Sensor Network (WSN) for agricultural applications has provided a platform for better decision making for crop planning and management, particularly in precision agriculture aspects. Due to the ever-increasing spread of WSNs there is a need for standards, i.e. a set of specifications and encodings to bring multiple sensor networks on common platform. Distributed sensor systems when brought together can facilitate better decision making in agricultural domain. The Open Geospatial Consortium (OGC) through Sensor Web Enablement (SWE) provides guidelines for semantic and syntactic standardization of sensor networks. In this work two distributed sensing systems (Agrisens and FieldServer) were selected to implement OGC SWE standards through a Service Oriented Architecture (SOA) approach. Online interoperable data processing was developed through SWE components such as Sensor Model Language (SensorML) and Sensor Observation Service (SOS). An integrated web client was developed to visualize the sensor observations and measurements that enables the retrieval of crop water resources availability and requirements in a systematic manner for both the sensing devices. Further, the client has also the ability to operate in an interoperable manner with any other OGC standardized WSN systems. The study of WSN systems has shown that there is need to augment the operations / processing capabilities of SOS in order to understand about collected sensor data and implement the modelling services. Also, the very low cost availability of WSN systems in future, it is possible to implement the OGC standardized SWE framework for agricultural applications with open source software tools.

  1. Network architectures and protocols for the integration of ACTS and ISDN

    NASA Technical Reports Server (NTRS)

    Chitre, D. M.; Lowry, P. A.

    1992-01-01

    A close integration of satellite networks and the integrated services digital network (ISDN) is essential for satellite networks to carry ISDN traffic effectively. This also shows how a given (pre-ISDN) satellite network architecture can be enhanced to handle ISDN signaling and provide ISDN services. It also describes the functional architecture and high-level protocols that could be implemented in the NASA Advanced Communications Technology Satellite (ACTS) low burst rate communications system to provide ISDN services.

  2. Simple Algorithms for Distributed Leader Election in Anonymous Synchronous Rings and Complete Networks Inspired by Neural Development in Fruit Flies.

    PubMed

    Xu, Lei; Jeavons, Peter

    2015-11-01

    Leader election in anonymous rings and complete networks is a very practical problem in distributed computing. Previous algorithms for this problem are generally designed for a classical message passing model where complex messages are exchanged. However, the need to send and receive complex messages makes such algorithms less practical for some real applications. We present some simple synchronous algorithms for distributed leader election in anonymous rings and complete networks that are inspired by the development of the neural system of the fruit fly. Our leader election algorithms all assume that only one-bit messages are broadcast by nodes in the network and processors are only able to distinguish between silence and the arrival of one or more messages. These restrictions allow implementations to use a simpler message-passing architecture. Even with these harsh restrictions our algorithms are shown to achieve good time and message complexity both analytically and experimentally.

  3. Deep neural network architectures for forecasting analgesic response.

    PubMed

    Nickerson, Paul; Tighe, Patrick; Shickel, Benjamin; Rashidi, Parisa

    2016-08-01

    Response to prescribed analgesic drugs varies between individuals, and choosing the right drug/dose often involves a lengthy, iterative process of trial and error. Furthermore, a significant portion of patients experience adverse events such as post-operative urinary retention (POUR) during inpatient management of acute postoperative pain. To better forecast analgesic responses, we compared conventional machine learning methods with modern neural network architectures to gauge their effectiveness at forecasting temporal patterns of postoperative pain and analgesic use, as well as predicting the risk of POUR. Our results indicate that simpler machine learning approaches might offer superior results; however, all of these techniques may play a promising role for developing smarter post-operative pain management strategies.

  4. Controlled architecture for improved macromolecular memory within polymer networks.

    PubMed

    DiPasquale, Stephen A; Byrne, Mark E

    2016-08-01

    This brief review analyzes recent developments in the field of living/controlled polymerization and the potential of this technique for creating imprinted polymers with highly structured architecture with macromolecular memory. As a result, it is possible to engineer polymers at the molecular level with increased homogeneity relating to enhanced template binding and transport. Only recently has living/controlled polymerization been exploited to decrease heterogeneity and substantially improve the efficiency of the imprinting process for both highly and weakly crosslinked imprinted polymers. Living polymerization can be utilized to create imprinted networks that are vastly more efficient than similar polymers produced using conventional free radical polymerization, and these improvements increase the role that macromolecular memory can play in the design and engineering of new drug delivery and sensing platforms.

  5. MASM: a market architecture for sensor management in distributed sensor networks

    NASA Astrophysics Data System (ADS)

    Viswanath, Avasarala; Mullen, Tracy; Hall, David; Garga, Amulya

    2005-03-01

    Rapid developments in sensor technology and its applications have energized research efforts towards devising a firm theoretical foundation for sensor management. Ubiquitous sensing, wide bandwidth communications and distributed processing provide both opportunities and challenges for sensor and process control and optimization. Traditional optimization techniques do not have the ability to simultaneously consider the wildly non-commensurate measures involved in sensor management in a single optimization routine. Market-oriented programming provides a valuable and principled paradigm to designing systems to solve this dynamic and distributed resource allocation problem. We have modeled the sensor management scenario as a competitive market, wherein the sensor manager holds a combinatorial auction to sell the various items produced by the sensors and the communication channels. However, standard auction mechanisms have been found not to be directly applicable to the sensor management domain. For this purpose, we have developed a specialized market architecture MASM (Market architecture for Sensor Management). In MASM, the mission manager is responsible for deciding task allocations to the consumers and their corresponding budgets and the sensor manager is responsible for resource allocation to the various consumers. In addition to having a modified combinatorial winner determination algorithm, MASM has specialized sensor network modules that address commensurability issues between consumers and producers in the sensor network domain. A preliminary multi-sensor, multi-target simulation environment has been implemented to test the performance of the proposed system. MASM outperformed the information theoretic sensor manager in meeting the mission objectives in the simulation experiments.

  6. NETRA: A parallel architecture for integrated vision systems 2: Algorithms and performance evaluation

    NASA Technical Reports Server (NTRS)

    Choudhary, Alok N.; Patel, Janak H.; Ahuja, Narendra

    1989-01-01

    In part 1 architecture of NETRA is presented. A performance evaluation of NETRA using several common vision algorithms is also presented. Performance of algorithms when they are mapped on one cluster is described. It is shown that SIMD, MIMD, and systolic algorithms can be easily mapped onto processor clusters, and almost linear speedups are possible. For some algorithms, analytical performance results are compared with implementation performance results. It is observed that the analysis is very accurate. Performance analysis of parallel algorithms when mapped across clusters is presented. Mappings across clusters illustrate the importance and use of shared as well as distributed memory in achieving high performance. The parameters for evaluation are derived from the characteristics of the parallel algorithms, and these parameters are used to evaluate the alternative communication strategies in NETRA. Furthermore, the effect of communication interference from other processors in the system on the execution of an algorithm is studied. Using the analysis, performance of many algorithms with different characteristics is presented. It is observed that if communication speeds are matched with the computation speeds, good speedups are possible when algorithms are mapped across clusters.

  7. Energy Aware Clustering Algorithms for Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Rakhshan, Noushin; Rafsanjani, Marjan Kuchaki; Liu, Chenglian

    2011-09-01

    The sensor nodes deployed in wireless sensor networks (WSNs) are extremely power constrained, so maximizing the lifetime of the entire networks is mainly considered in the design. In wireless sensor networks, hierarchical network structures have the advantage of providing scalable and energy efficient solutions. In this paper, we investigate different clustering algorithms for WSNs and also compare these clustering algorithms based on metrics such as clustering distribution, cluster's load balancing, Cluster Head's (CH) selection strategy, CH's role rotation, node mobility, clusters overlapping, intra-cluster communications, reliability, security and location awareness.

  8. Optimization of multicast optical networks with genetic algorithm

    NASA Astrophysics Data System (ADS)

    Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng

    2007-11-01

    In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.

  9. An Open Distributed Architecture for Sensor Networks for Risk Management.

    PubMed

    Douglas, John; Usländer, Thomas; Schimak, Gerald; Esteban, J Fernando; Denzer, Ralf

    2008-03-13

    Sensors provide some of the basic input data for risk management of natural andman-made hazards. Here the word 'sensors' covers everything from remote sensingsatellites, providing invaluable images of large regions, through instruments installed on theEarth's surface to instruments situated in deep boreholes and on the sea floor, providinghighly-detailed point-based information from single sites. Data from such sensors is used inall stages of risk management, from hazard, vulnerability and risk assessment in the preeventphase, information to provide on-site help during the crisis phase through to data toaid in recovery following an event. Because data from sensors play such an important part inimproving understanding of the causes of risk and consequently in its mitigation,considerable investment has been made in the construction and maintenance of highlysophisticatedsensor networks. In spite of the ubiquitous need for information from sensornetworks, the use of such data is hampered in many ways. Firstly, information about thepresence and capabilities of sensor networks operating in a region is difficult to obtain dueto a lack of easily available and usable meta-information. Secondly, once sensor networkshave been identified their data it is often difficult to access due to a lack of interoperability between dissemination and acquisition systems. Thirdly, the transfer and processing ofinformation from sensors is limited, again by incompatibilities between systems. Therefore,the current situation leads to a lack of efficiency and limited use of the available data thathas an important role to play in risk mitigation. In view of this situation, the EuropeanCommission (EC) is funding a number of Integrated Projects within the Sixth FrameworkProgramme concerned with improving the accessibility of data and services for riskmanagement. Two of these projects: 'Open Architecture and Spatial Data Infrastructure forRisk Management' (ORCHESTRA, http

  10. Advanced Algorithms for Local Routing Strategy on Complex Networks

    PubMed Central

    Lin, Benchuan; Chen, Bokui; Gao, Yachun; Tse, Chi K.; Dong, Chuanfei; Miao, Lixin; Wang, Binghong

    2016-01-01

    Despite the significant improvement on network performance provided by global routing strategies, their applications are still limited to small-scale networks, due to the need for acquiring global information of the network which grows and changes rapidly with time. Local routing strategies, however, need much less local information, though their transmission efficiency and network capacity are much lower than that of global routing strategies. In view of this, three algorithms are proposed and a thorough investigation is conducted in this paper. These algorithms include a node duplication avoidance algorithm, a next-nearest-neighbor algorithm and a restrictive queue length algorithm. After applying them to typical local routing strategies, the critical generation rate of information packets Rc increases by over ten-fold and the average transmission time 〈T〉 decreases by 70–90 percent, both of which are key physical quantities to assess the efficiency of routing strategies on complex networks. More importantly, in comparison with global routing strategies, the improved local routing strategies can yield better network performance under certain circumstances. This is a revolutionary leap for communication networks, because local routing strategy enjoys great superiority over global routing strategy not only in terms of the reduction of computational expense, but also in terms of the flexibility of implementation, especially for large-scale networks. PMID:27434502

  11. An Improved Back Propagation Neural Network Algorithm on Classification Problems

    NASA Astrophysics Data System (ADS)

    Nawi, Nazri Mohd; Ransing, R. S.; Salleh, Mohd Najib Mohd; Ghazali, Rozaida; Hamid, Norhamreeza Abdul

    The back propagation algorithm is one the most popular algorithms to train feed forward neural networks. However, the convergence of this algorithm is slow, it is mainly because of gradient descent algorithm. Previous research demonstrated that in 'feed forward' algorithm, the slope of the activation function is directly influenced by a parameter referred to as 'gain'. This research proposed an algorithm for improving the performance of the back propagation algorithm by introducing the adaptive gain of the activation function. The gain values change adaptively for each node. The influence of the adaptive gain on the learning ability of a neural network is analysed. Multi layer feed forward neural networks have been assessed. Physical interpretation of the relationship between the gain value and the learning rate and weight values is given. The efficiency of the proposed algorithm is compared with conventional Gradient Descent Method and verified by means of simulation on four classification problems. In learning the patterns, the simulations result demonstrate that the proposed method converged faster on Wisconsin breast cancer with an improvement ratio of nearly 2.8, 1.76 on diabetes problem, 65% better on thyroid data sets and 97% faster on IRIS classification problem. The results clearly show that the proposed algorithm significantly improves the learning speed of the conventional back-propagation algorithm.

  12. A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks

    PubMed Central

    Yang, Liu; Lu, Yinzhi; Zhong, Yuanchang; Wu, Xuegang; Yang, Simon X.

    2015-01-01

    Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs) because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC) algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs) in the network act as routers to transmit data to base station (BS) cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols. PMID:26712764

  13. A Multi-Hop Energy Neutral Clustering Algorithm for Maximizing Network Information Gathering in Energy Harvesting Wireless Sensor Networks.

    PubMed

    Yang, Liu; Lu, Yinzhi; Zhong, Yuanchang; Wu, Xuegang; Yang, Simon X

    2015-12-26

    Energy resource limitation is a severe problem in traditional wireless sensor networks (WSNs) because it restricts the lifetime of network. Recently, the emergence of energy harvesting techniques has brought with them the expectation to overcome this problem. In particular, it is possible for a sensor node with energy harvesting abilities to work perpetually in an Energy Neutral state. In this paper, a Multi-hop Energy Neutral Clustering (MENC) algorithm is proposed to construct the optimal multi-hop clustering architecture in energy harvesting WSNs, with the goal of achieving perpetual network operation. All cluster heads (CHs) in the network act as routers to transmit data to base station (BS) cooperatively by a multi-hop communication method. In addition, by analyzing the energy consumption of intra- and inter-cluster data transmission, we give the energy neutrality constraints. Under these constraints, every sensor node can work in an energy neutral state, which in turn provides perpetual network operation. Furthermore, the minimum network data transmission cycle is mathematically derived using convex optimization techniques while the network information gathering is maximal. Simulation results show that our protocol can achieve perpetual network operation, so that the consistent data delivery is guaranteed. In addition, substantial improvements on the performance of network throughput are also achieved as compared to the famous traditional clustering protocol LEACH and recent energy harvesting aware clustering protocols.

  14. Implementation of Karp-Rabin string matching algorithm in reconfigurable hardware for network intrusion prevention system

    NASA Astrophysics Data System (ADS)

    Botwicz, Jakub; Buciak, Piotr; Sapiecha, Piotr

    2006-03-01

    Intrusion Prevention Systems (IPSs) have become widely recognized as a powerful tool and an important element of IT security safeguards. The essential feature of network IPSs is searching through network packets and matching multiple strings, that are fingerprints of known attacks. String matching is highly resource consuming and also the most significant bottleneck of IPSs. In this article an extension of the classical Karp-Rabin algorithm and its implementation architectures were examined. The result is a software, which generates a source code of a string matching module in hardware description language, that could be easily used to create an Intrusion Prevention System implemented in reconfigurable hardware. The prepared module matches the complete set of Snort IPS signatures achieving throughput of over 2 Gbps on an Altera Stratix I1 evaluation board. The most significant advantage of the proposed architecture is that the update of the patterns database does not require reconfiguration of the circuitry.

  15. Implementing wireless sensor networks for architectural heritage conservation

    NASA Astrophysics Data System (ADS)

    Martínez-Garrido, M. I.; Aparicio, S.; Fort, R.; Izquierdo, M. A. G.; Anaya, J. J.

    2012-04-01

    Preventive conservation in architectural heritage is one of the most important aims for the development and implementation of new techniques to assess decay, lending to reduce damage before it has occurred and reducing costs in the long term. For that purpose, it is necessary to know all aspects influencing in decay evolution depending on the material under study and its internal and external conditions. Wireless sensor networks are an emerging technology and a minimally invasive technique. The use of these networks facilitates data acquisition and monitoring of a large number of variables that could provoke material damages, such as presence of harmful compounds like salts, dampness, etc. The current project presents different wireless sensors networks (WSN) and sensors used to fulfill the requirements for a complete analysis of main decay agents in a Renaissance church of the 16th century in Madrid (Spain). Current typologies and wireless technologies are studied establishing the most suitable system and the convenience of each one. Firstly, it is very important to consider that microclimate is in close correlation with material deterioration. Therefore a temperature(T) and relative humidity (RH)/moisture network has been developed, using ZigBee wireless communications protocols, and monitoring different points along the church surface. These points are recording RH/T differences depending on the height and the sensor location (inside the material or on the surface). On the other hand, T/RH button sensors have been used, minimizing aesthetical interferences, and concluding which is the most advisable way for monitoring these specific parameters. Due to the fact that microclimate is a complex phenomenon, it is necessary to examine spatial distribution and time evolution at the same time. This work shows both studies since the development expects a long term monitoring. A different wireless network has been deployed to study the effects of pollution caused by other

  16. Overview of the Smart Network Element Architecture and Recent Innovations

    NASA Technical Reports Server (NTRS)

    Perotti, Jose M.; Mata, Carlos T.; Oostdyk, Rebecca L.

    2008-01-01

    In industrial environments, system operators rely on the availability and accuracy of sensors to monitor processes and detect failures of components and/or processes. The sensors must be networked in such a way that their data is reported to a central human interface, where operators are tasked with making real-time decisions based on the state of the sensors and the components that are being monitored. Incorporating health management functions at this central location aids the operator by automating the decision-making process to suggest, and sometimes perform, the action required by current operating conditions. Integrated Systems Health Management (ISHM) aims to incorporate data from many sources, including real-time and historical data and user input, and extract information and knowledge from that data to diagnose failures and predict future failures of the system. By distributing health management processing to lower levels of the architecture, there is less bandwidth required for ISHM, enhanced data fusion, make systems and processes more robust, and improved resolution for the detection and isolation of failures in a system, subsystem, component, or process. The Smart Network Element (SNE) has been developed at NASA Kennedy Space Center to perform intelligent functions at sensors and actuators' level in support of ISHM.

  17. Hybrid Network Architectures for the Next Generation NAS

    NASA Technical Reports Server (NTRS)

    Madubata, Christian

    2003-01-01

    To meet the needs of the 21st Century NAS, an integrated, network-centric infrastructure is essential that is characterized by secure, high bandwidth, digital communication systems that support precision navigation capable of reducing position errors for all aircraft to within a few meters. This system will also require precision surveillance systems capable of accurately locating all aircraft, and automatically detecting any deviations from an approved path within seconds and be able to deliver high resolution weather forecasts - critical to create 4- dimensional (space and time) profiles for up to 6 hours for all atmospheric conditions affecting aviation, including wake vortices. The 21st Century NAS will be characterized by highly accurate digital data bases depicting terrain, obstacle, and airport information no matter what visibility conditions exist. This research task will be to perform a high-level requirements analysis of the applications, information and services required by the next generation National Airspace System. The investigation and analysis is expected to lead to the development and design of several national network-centric communications architectures that would be capable of supporting the Next Generation NAS.

  18. Traffic Management Algorithms in Wireless Sensor Networks

    DTIC Science & Technology

    2006-09-01

    pages 48–51, Los Angeles, February 2003. [4] Ian F. Akyildiz, Weilian Su, Yogesg Sankarasubramaniam, and Erdal Cayirci. A survey on sensor networks... Mehmet C. Vuran, B. Akan, and Ian F. Akyildiz. Spatio-temporal correlation: theory and applications for wireless sensor networks. Computer Networks

  19. Criteria for Evaluating Alternative Network and Link Layer Protocols for the NASA Constellation Program Communication Architecture

    NASA Technical Reports Server (NTRS)

    Benbenek, Daniel; Soloff, Jason; Lieb, Erica

    2010-01-01

    Selecting a communications and network architecture for future manned space flight requires an evaluation of the varying goals and objectives of the program, development of communications and network architecture evaluation criteria, and assessment of critical architecture trades. This paper uses Cx Program proposed exploration activities as a guideline; lunar sortie, outpost, Mars, and flexible path options are described. A set of proposed communications network architecture criteria are proposed and described. They include: interoperability, security, reliability, and ease of automating topology changes. Finally a key set of architecture options are traded including (1) multiplexing data at a common network layer vs. at the data link layer, (2) implementing multiple network layers vs. a single network layer, and (3) the use of a particular network layer protocol, primarily IPv6 vs. Delay Tolerant Networking (DTN). In summary, the protocol options are evaluated against the proposed exploration activities and their relative performance with respect to the criteria are assessed. An architectural approach which includes (a) the capability of multiplexing at both the network layer and the data link layer and (b) a single network layer for operations at each program phase, as these solutions are best suited to respond to the widest array of program needs and meet each of the evaluation criteria.

  20. An Algorithm to Find Minimum Cost Flow in a Network.

    DTIC Science & Technology

    The paper describes an algorithm to find a flow of minimum cost in a network and a computer program which performs this algorithm. The principle of...optimal. What might make this algorithm efficient is the fact that the information which has been gathered at some iteration to find a negative...circuit will be used to find a negative circuit at next iteration so that each iteration requires a relatively small amount of work. (Author)

  1. A double herd krill based algorithm for location area optimization in mobile wireless cellular network.

    PubMed

    Vincylloyd, F; Anand, B

    2015-01-01

    In wireless communication systems, mobility tracking deals with determining a mobile subscriber (MS) covering the area serviced by the wireless network. Tracking a mobile subscriber is governed by the two fundamental components called location updating (LU) and paging. This paper presents a novel hybrid method using a krill herd algorithm designed to optimize the location area (LA) within available spectrum such that total network cost, comprising location update (LU) cost and cost for paging, is minimized without compromise. Based on various mobility patterns of users and network architecture, the design of the LR area is formulated as a combinatorial optimization problem. Numerical results indicate that the proposed model provides a more accurate update boundary in real environment than that derived from a hexagonal cell configuration with a random walk movement pattern. The proposed model allows the network to maintain a better balance between the processing incurred due to location update and the radio bandwidth utilized for paging between call arrivals.

  2. A class of least-squares filtering and identification algorithms with systolic array architectures

    NASA Technical Reports Server (NTRS)

    Kalson, Seth Z.; Yao, Kung

    1991-01-01

    A unified approach is presented for deriving a large class of new and previously known time- and order-recursive least-squares algorithms with systolic array architectures, suitable for high-throughput-rate and VLSI implementations of space-time filtering and system identification problems. The geometrical derivation given is unique in that no assumption is made concerning the rank of the sample data correlation matrix. This method utilizes and extends the concept of oblique projections, as used previously in the derivations of the least-squares lattice algorithms. Exponentially weighted least-squares criteria are considered for both sliding and growing memory.

  3. Reliable and Efficient Parallel Processing Algorithms and Architectures for Modern Signal Processing. Ph.D. Thesis

    NASA Technical Reports Server (NTRS)

    Liu, Kuojuey Ray

    1990-01-01

    Least-squares (LS) estimations and spectral decomposition algorithms constitute the heart of modern signal processing and communication problems. Implementations of recursive LS and spectral decomposition algorithms onto parallel processing architectures such as systolic arrays with efficient fault-tolerant schemes are the major concerns of this dissertation. There are four major results in this dissertation. First, we propose the systolic block Householder transformation with application to the recursive least-squares minimization. It is successfully implemented on a systolic array with a two-level pipelined implementation at the vector level as well as at the word level. Second, a real-time algorithm-based concurrent error detection scheme based on the residual method is proposed for the QRD RLS systolic array. The fault diagnosis, order degraded reconfiguration, and performance analysis are also considered. Third, the dynamic range, stability, error detection capability under finite-precision implementation, order degraded performance, and residual estimation under faulty situations for the QRD RLS systolic array are studied in details. Finally, we propose the use of multi-phase systolic algorithms for spectral decomposition based on the QR algorithm. Two systolic architectures, one based on triangular array and another based on rectangular array, are presented for the multiphase operations with fault-tolerant considerations. Eigenvectors and singular vectors can be easily obtained by using the multi-pase operations. Performance issues are also considered.

  4. Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes

    PubMed Central

    Borgs, Christian; Chayes, Jennifer T.; Ingrosso, Alessandro; Lucibello, Carlo; Saglietti, Luca; Zecchina, Riccardo

    2016-01-01

    In artificial neural networks, learning from data is a computationally demanding task in which a large number of connection weights are iteratively tuned through stochastic-gradient-based heuristic processes over a cost function. It is not well understood how learning occurs in these systems, in particular how they avoid getting trapped in configurations with poor computational performance. Here, we study the difficult case of networks with discrete weights, where the optimization landscape is very rough even for simple architectures, and provide theoretical and numerical evidence of the existence of rare—but extremely dense and accessible—regions of configurations in the network weight space. We define a measure, the robust ensemble (RE), which suppresses trapping by isolated configurations and amplifies the role of these dense regions. We analytically compute the RE in some exactly solvable models and also provide a general algorithmic scheme that is straightforward to implement: define a cost function given by a sum of a finite number of replicas of the original cost function, with a constraint centering the replicas around a driving assignment. To illustrate this, we derive several powerful algorithms, ranging from Markov Chains to message passing to gradient descent processes, where the algorithms target the robust dense states, resulting in substantial improvements in performance. The weak dependence on the number of precision bits of the weights leads us to conjecture that very similar reasoning applies to more conventional neural networks. Analogous algorithmic schemes can also be applied to other optimization problems. PMID:27856745

  5. Hardware architecture for projective model calculation and false match refining using random sample consensus algorithm

    NASA Astrophysics Data System (ADS)

    Azimi, Ehsan; Behrad, Alireza; Ghaznavi-Ghoushchi, Mohammad Bagher; Shanbehzadeh, Jamshid

    2016-11-01

    The projective model is an important mapping function for the calculation of global transformation between two images. However, its hardware implementation is challenging because of a large number of coefficients with different required precisions for fixed point representation. A VLSI hardware architecture is proposed for the calculation of a global projective model between input and reference images and refining false matches using random sample consensus (RANSAC) algorithm. To make the hardware implementation feasible, it is proved that the calculation of the projective model can be divided into four submodels comprising two translations, an affine model and a simpler projective mapping. This approach makes the hardware implementation feasible and considerably reduces the required number of bits for fixed point representation of model coefficients and intermediate variables. The proposed hardware architecture for the calculation of a global projective model using the RANSAC algorithm was implemented using Verilog hardware description language and the functionality of the design was validated through several experiments. The proposed architecture was synthesized by using an application-specific integrated circuit digital design flow utilizing 180-nm CMOS technology as well as a Virtex-6 field programmable gate array. Experimental results confirm the efficiency of the proposed hardware architecture in comparison with software implementation.

  6. Neural network architecture for cognitive navigation in dynamic environments.

    PubMed

    Villacorta-Atienza, José Antonio; Makarov, Valeri A

    2013-12-01

    Navigation in time-evolving environments with moving targets and obstacles requires cognitive abilities widely demonstrated by even simplest animals. However, it is a long-standing challenging problem for artificial agents. Cognitive autonomous robots coping with this problem must solve two essential tasks: 1) understand the environment in terms of what may happen and how I can deal with this and 2) learn successful experiences for their further use in an automatic subconscious way. The recently introduced concept of compact internal representation (CIR) provides the ground for both the tasks. CIR is a specific cognitive map that compacts time-evolving situations into static structures containing information necessary for navigation. It belongs to the class of global approaches, i.e., it finds trajectories to a target when they exist but also detects situations when no solution can be found. Here we extend the concept of situations with mobile targets. Then using CIR as a core, we propose a closed-loop neural network architecture consisting of conscious and subconscious pathways for efficient decision-making. The conscious pathway provides solutions to novel situations if the default subconscious pathway fails to guide the agent to a target. Employing experiments with roving robots and numerical simulations, we show that the proposed architecture provides the robot with cognitive abilities and enables reliable and flexible navigation in realistic time-evolving environments. We prove that the subconscious pathway is robust against uncertainty in the sensory information. Thus if a novel situation is similar but not identical to the previous experience (because of, e.g., noisy perception) then the subconscious pathway is able to provide an effective solution.

  7. T-SDN architecture for space and ground integrated optical transport network

    NASA Astrophysics Data System (ADS)

    Nie, Kunkun; Hu, Wenjing; Gao, Shenghua; Chang, Chengwu

    2015-11-01

    Integrated optical transport network is the development trend of the future space information backbone network. The space and ground integrated optical transport network(SGIOTN) may contain a variety of equipment and systems. Changing the network or meeting some innovation missions in the network will be an expensive implement. Software Defined Network(SDN) provides a good solution to flexibly adding process logic, timely control states and resources of the whole network, as well as shielding the differences of heterogeneous equipment and so on. According to the characteristics of SGIOTN, we propose an transport SDN architecture for it, with hierarchical control plane and data plane composed of packet networks and optical transport networks.

  8. Tomosynthesis-detected Architectural Distortion: Management Algorithm with Radiologic-Pathologic Correlation.

    PubMed

    Durand, Melissa A; Wang, Steven; Hooley, Regina J; Raghu, Madhavi; Philpotts, Liane E

    2016-01-01

    As use of digital breast tomosynthesis becomes increasingly widespread, new management challenges are inevitable because tomosynthesis may reveal suspicious lesions not visible at conventional two-dimensional (2D) full-field digital mammography. Architectural distortion is a mammographic finding associated with a high positive predictive value for malignancy. It is detected more frequently at tomosynthesis than at 2D digital mammography and may even be occult at conventional 2D imaging. Few studies have focused on tomosynthesis-detected architectural distortions to date, and optimal management of these distortions has yet to be well defined. Since implementing tomosynthesis at our institution in 2011, we have learned some practical ways to assess architectural distortion. Because distortions may be subtle, tomosynthesis localization tools plus improved visualization of adjacent landmarks are crucial elements in guiding mammographic identification of elusive distortions. These same tools can guide more focused ultrasonography (US) of the breast, which facilitates detection and permits US-guided tissue sampling. Some distortions may be sonographically occult, in which case magnetic resonance imaging may be a reasonable option, both to increase diagnostic confidence and to provide a means for image-guided biopsy. As an alternative, tomosynthesis-guided biopsy, conventional stereotactic biopsy (when possible), or tomosynthesis-guided needle localization may be used to achieve tissue diagnosis. Practical uses for tomosynthesis in evaluation of architectural distortion are highlighted, potential complications are identified, and a working algorithm for management of tomosynthesis-detected architectural distortion is proposed.

  9. A Study of a Network-Flow Algorithm and a Noncorrecting Algorithm for Test Assembly.

    ERIC Educational Resources Information Center

    Armstrong, R. D.; And Others

    1996-01-01

    When the network-flow algorithm (NFA) and the average growth approximation algorithm (AGAA) were used for automated test assembly with American College Test and Armed Services Vocational Aptitude Battery item banks, results indicate that reasonable error in item parameters is not harmful for test assembly using NFA or AGAA. (SLD)

  10. A computational study of routing algorithms for realistic transportation networks

    SciTech Connect

    Jacob, R.; Marathe, M.V.; Nagel, K.

    1998-12-01

    The authors carry out an experimental analysis of a number of shortest path (routing) algorithms investigated in the context of the TRANSIMS (Transportation Analysis and Simulation System) project. The main focus of the paper is to study how various heuristic and exact solutions, associated data structures affected the computational performance of the software developed especially for realistic transportation networks. For this purpose the authors have used Dallas Fort-Worth road network with very high degree of resolution. The following general results are obtained: (1) they discuss and experimentally analyze various one-one shortest path algorithms, which include classical exact algorithms studied in the literature as well as heuristic solutions that are designed to take into account the geometric structure of the input instances; (2) they describe a number of extensions to the basic shortest path algorithm. These extensions were primarily motivated by practical problems arising in TRANSIMS and ITS (Intelligent Transportation Systems) related technologies. Extensions discussed include--(i) time dependent networks, (ii) multi-modal networks, (iii) networks with public transportation and associated schedules. Computational results are provided to empirically compare the efficiency of various algorithms. The studies indicate that a modified Dijkstra`s algorithm is computationally fast and an excellent candidate for use in various transportation planning applications as well as ITS related technologies.

  11. Generic architecture for real-time multisensor fusion tracking algorithm development and evaluation

    NASA Astrophysics Data System (ADS)

    Queeney, Tom; Woods, Edward

    1994-10-01

    Westinghouse has developed and demonstrated a system for the rapid prototyping of Sensor Fusion Tracking (SFT) algorithms. The system provides an object-oriented envelope with three sets of generic software objects to aid in the development and evaluation of SFT algorithms. The first is a generic tracker model that encapsulates the idea of a tracker being a series of SFT algorithms along with the data manipulated by those algorithms and is capable of simultaneously supporting multiple, independent trackers. The second is a set of flexible, easily extensible sensor and target models which allows many types of sensors and targets to be used. Live, recorded and simulated sensors and combinations thereof can be utilized as sources for the trackers. The sensor models also provide an easily extensible interface to the generic tracker model so that all sensors provide input to the SFT algorithms in the same fashion. The third is a highly versatile display and user interface that allows easy access to many of the performance measures for sensors and trackers for easy evaluation and debugging of the SFT algorithms. The system is an object-oriented design programmed in C++. This system with several of the SFT algorithms developed for it has been used with live sensors as a real-time tracking system. This paper outlines the salient features of the sensor fusion architecture and programming environment.

  12. Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data

    NASA Technical Reports Server (NTRS)

    Rajkumar, T.; Bardina, Jorge; Clancy, Daniel (Technical Monitor)

    2002-01-01

    coefficients to an accuracy of 110% . In our problem, we would like to get an optimized neural network architecture and minimum data set. This has been accomplished within 500 training cycles of a neural network. After removing training pairs (outliers), the GA has produced much better results. The neural network constructed is a feed forward neural network with a back propagation learning mechanism. The main goal has been to free the network design process from constraints of human biases, and to discover better forms of neural network architectures. The automation of the network architecture search by genetic algorithms seems to have been the best way to achieve this goal.

  13. A novel unicast routing algorithm for LEO satellite networks

    NASA Astrophysics Data System (ADS)

    Song, Xuegui; Liu, Kai; Zhang, Jun; Cheng, Lianzhen

    2005-11-01

    One of research challenges in low earth orbit (LEO) satellite networks is to develop specialized and efficient routing algorithms. In this paper, a dynamic source routing algorithm (DSRA) for LEO satellite networks is presented to achieve short end-to-end delay and low computation overhead under the condition that a logical location concept is adopted to hide satellite mobility. In this algorithm, the path between source and destination with minimum propagation delay is designated by source satellite in packet header by an efficient metric < D0, n0; D1, n1; D2, n2 >. Then the packet is forwarded to its destination by intermediate nodes according to the metric. The performance of the algorithm is evaluated through simulation and its computation complexity is analyzed to validate algorithm efficiency.

  14. Accurate and efficient maximal ball algorithm for pore network extraction

    NASA Astrophysics Data System (ADS)

    Arand, Frederick; Hesser, Jürgen

    2017-04-01

    The maximal ball (MB) algorithm is a well established method for the morphological analysis of porous media. It extracts a network of pores and throats from volumetric data. This paper describes structural modifications to the algorithm, while the basic concepts are preserved. Substantial improvements to accuracy and efficiency are achieved as follows: First, all calculations are performed on a subvoxel accurate distance field, and no approximations to discretize balls are made. Second, data structures are simplified to keep memory usage low and improve algorithmic speed. Third, small and reasonable adjustments increase speed significantly. In volumes with high porosity, memory usage is improved compared to classic MB algorithms. Furthermore, processing is accelerated more than three times. Finally, the modified MB algorithm is verified by extracting several network properties from reference as well as real data sets. Runtimes are measured and compared to literature.

  15. Analysis of adaptive algorithms for an integrated communication network

    NASA Technical Reports Server (NTRS)

    Reed, Daniel A.; Barr, Matthew; Chong-Kwon, Kim

    1985-01-01

    Techniques were examined that trade communication bandwidth for decreased transmission delays. When the network is lightly used, these schemes attempt to use additional network resources to decrease communication delays. As the network utilization rises, the schemes degrade gracefully, still providing service but with minimal use of the network. Because the schemes use a combination of circuit and packet switching, they should respond to variations in the types and amounts of network traffic. Also, a combination of circuit and packet switching to support the widely varying traffic demands imposed on an integrated network was investigated. The packet switched component is best suited to bursty traffic where some delays in delivery are acceptable. The circuit switched component is reserved for traffic that must meet real time constraints. Selected packet routing algorithms that might be used in an integrated network were simulated. An integrated traffic places widely varying workload demands on a network. Adaptive algorithms were identified, ones that respond to both the transient and evolutionary changes that arise in integrated networks. A new algorithm was developed, hybrid weighted routing, that adapts to workload changes.

  16. The probabilistic neural network architecture for high speed classification of remotely sensed imagery

    NASA Technical Reports Server (NTRS)

    Chettri, Samir R.; Cromp, Robert F.

    1993-01-01

    In this paper we discuss a neural network architecture (the Probabilistic Neural Net or the PNN) that, to the best of our knowledge, has not previously been applied to remotely sensed data. The PNN is a supervised non-parametric classification algorithm as opposed to the Gaussian maximum likelihood classifier (GMLC). The PNN works by fitting a Gaussian kernel to each training point. The width of the Gaussian is controlled by a tuning parameter called the window width. If very small widths are used, the method is equivalent to the nearest neighbor method. For large windows, the PNN behaves like the GMLC. The basic implementation of the PNN requires no training time at all. In this respect it is far better than the commonly used backpropagation neural network which can be shown to take O(N6) time for training where N is the dimensionality of the input vector. In addition the PNN can be implemented in a feed forward mode in hardware. The disadvantage of the PNN is that it requires all the training data to be stored. Some solutions to this problem are discussed in the paper. Finally, we discuss the accuracy of the PNN with respect to the GMLC and the backpropagation neural network (BPNN). The PNN is shown to be better than GMLC and not as good as the BPNN with regards to classification accuracy.

  17. The guitar chord-generating algorithm based on complex network

    NASA Astrophysics Data System (ADS)

    Ren, Tao; Wang, Yi-fan; Du, Dan; Liu, Miao-miao; Siddiqi, Awais

    2016-02-01

    This paper aims to generate chords for popular songs automatically based on complex network. Firstly, according to the characteristics of guitar tablature, six chord networks of popular songs by six pop singers are constructed and the properties of all networks are concluded. By analyzing the diverse chord networks, the accompaniment regulations and features are shown, with which the chords can be generated automatically. Secondly, in terms of the characteristics of popular songs, a two-tiered network containing a verse network and a chorus network is constructed. With this network, the verse and chorus can be composed respectively with the random walk algorithm. Thirdly, the musical motif is considered for generating chords, with which the bad chord progressions can be revised. This method can make the accompaniments sound more melodious. Finally, a popular song is chosen for generating chords and the new generated accompaniment sounds better than those done by the composers.

  18. Data Compression Algorithm Architecture for Large Depth-of-Field Particle Image Velocimeters

    NASA Technical Reports Server (NTRS)

    Bos, Brent; Memarsadeghi, Nargess; Kizhner, Semion; Antonille, Scott

    2013-01-01

    A large depth-of-field particle image velocimeter (PIV) is designed to characterize dynamic dust environments on planetary surfaces. This instrument detects lofted dust particles, and senses the number of particles per unit volume, measuring their sizes, velocities (both speed and direction), and shape factors when the particles are large. To measure these particle characteristics in-flight, the instrument gathers two-dimensional image data at a high frame rate, typically >4,000 Hz, generating large amounts of data for every second of operation, approximately 6 GB/s. To characterize a planetary dust environment that is dynamic, the instrument would have to operate for at least several minutes during an observation period, easily producing more than a terabyte of data per observation. Given current technology, this amount of data would be very difficult to store onboard a spacecraft, and downlink to Earth. Since 2007, innovators have been developing an autonomous image analysis algorithm architecture for the PIV instrument to greatly reduce the amount of data that it has to store and downlink. The algorithm analyzes PIV images and automatically reduces the image information down to only the particle measurement data that is of interest, reducing the amount of data that is handled by more than 10(exp 3). The state of development for this innovation is now fairly mature, with a functional algorithm architecture, along with several key pieces of algorithm logic, that has been proven through field test data acquired with a proof-of-concept PIV instrument.

  19. A stochastic learning algorithm for layered neural networks

    SciTech Connect

    Bartlett, E.B.; Uhrig, R.E.

    1992-12-31

    The random optimization method typically uses a Gaussian probability density function (PDF) to generate a random search vector. In this paper the random search technique is applied to the neural network training problem and is modified to dynamically seek out the optimal probability density function (OPDF) from which to select the search vector. The dynamic OPDF search process, combined with an auto-adaptive stratified sampling technique and a dynamic node architecture (DNA) learning scheme, completes the modifications of the basic method. The DNA technique determines the appropriate number of hidden nodes needed for a given training problem. By using DNA, researchers do not have to set the neural network architectures before training is initiated. The approach is applied to networks of generalized, fully interconnected, continuous perceptions. Computer simulation results are given.

  20. Parallel Algorithms for Monte Carlo Particle Transport Simulation on Exascale Computing Architectures

    NASA Astrophysics Data System (ADS)

    Romano, Paul Kollath

    Monte Carlo particle transport methods are being considered as a viable option for high-fidelity simulation of nuclear reactors. While Monte Carlo methods offer several potential advantages over deterministic methods, there are a number of algorithmic shortcomings that would prevent their immediate adoption for full-core analyses. In this thesis, algorithms are proposed both to ameliorate the degradation in parallel efficiency typically observed for large numbers of processors and to offer a means of decomposing large tally data that will be needed for reactor analysis. A nearest-neighbor fission bank algorithm was proposed and subsequently implemented in the OpenMC Monte Carlo code. A theoretical analysis of the communication pattern shows that the expected cost is O( N ) whereas traditional fission bank algorithms are O(N) at best. The algorithm was tested on two supercomputers, the Intrepid Blue Gene/P and the Titan Cray XK7, and demonstrated nearly linear parallel scaling up to 163,840 processor cores on a full-core benchmark problem. An algorithm for reducing network communication arising from tally reduction was analyzed and implemented in OpenMC. The proposed algorithm groups only particle histories on a single processor into batches for tally purposes---in doing so it prevents all network communication for tallies until the very end of the simulation. The algorithm was tested, again on a full-core benchmark, and shown to reduce network communication substantially. A model was developed to predict the impact of load imbalances on the performance of domain decomposed simulations. The analysis demonstrated that load imbalances in domain decomposed simulations arise from two distinct phenomena: non-uniform particle densities and non-uniform spatial leakage. The dominant performance penalty for domain decomposition was shown to come from these physical effects rather than insufficient network bandwidth or high latency. The model predictions were verified with

  1. 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

  2. Online learning algorithm for time series forecasting suitable for low cost wireless sensor networks nodes.

    PubMed

    Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

    2015-04-21

    Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

  3. Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

    PubMed Central

    Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

    2015-01-01

    Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources. PMID:25905698

  4. A distributed geo-routing algorithm for wireless sensor networks.

    PubMed

    Joshi, Gyanendra Prasad; Kim, Sung Won

    2009-01-01

    Geographic wireless sensor networks use position information for greedy routing. Greedy routing works well in dense networks, whereas in sparse networks it may fail and require a recovery algorithm. Recovery algorithms help the packet to get out of the communication void. However, these algorithms are generally costly for resource constrained position-based wireless sensor networks (WSNs). In this paper, we propose a void avoidance algorithm (VAA), a novel idea based on upgrading virtual distance. VAA allows wireless sensor nodes to remove all stuck nodes by transforming the routing graph and forwarding packets using only greedy routing. In VAA, the stuck node upgrades distance unless it finds a next hop node that is closer to the destination than it is. VAA guarantees packet delivery if there is a topologically valid path. Further, it is completely distributed, immediately responds to node failure or topology changes and does not require planarization of the network. NS-2 is used to evaluate the performance and correctness of VAA and we compare its performance to other protocols. Simulations show our proposed algorithm consumes less energy, has an efficient path and substantially less control overheads.

  5. A Danger-Theory-Based Immune Network Optimization Algorithm

    PubMed Central

    Li, Tao; Xiao, Xin; Shi, Yuanquan

    2013-01-01

    Existing artificial immune optimization algorithms reflect a number of shortcomings, such as premature convergence and poor local search ability. This paper proposes a danger-theory-based immune network optimization algorithm, named dt-aiNet. The danger theory emphasizes that danger signals generated from changes of environments will guide different levels of immune responses, and the areas around danger signals are called danger zones. By defining the danger zone to calculate danger signals for each antibody, the algorithm adjusts antibodies' concentrations through its own danger signals and then triggers immune responses of self-regulation. So the population diversity can be maintained. Experimental results show that the algorithm has more advantages in the solution quality and diversity of the population. Compared with influential optimization algorithms, CLONALG, opt-aiNet, and dopt-aiNet, the algorithm has smaller error values and higher success rates and can find solutions to meet the accuracies within the specified function evaluation times. PMID:23483853

  6. General inference algorithm of Bayesian networks based on clique tree

    NASA Astrophysics Data System (ADS)

    Li, Haijun; Liu, Xiao

    2008-10-01

    A general inference algorithm which based on exact algorithm of clique tree and importance sampling principle was put forward this article. It applied advantages of two algorithms, made information transfer from one clique to another, but don't calculate exact interim result. It calculated and dealt with the information using approximate algorithm, calculated the information from one clique to another using current potential. Because this algorithm was an iterative course of improvement, this continuous ran could increases potential of each clique, and produced much more exact information. Hybrid Bayesian Networks inference algorithm based on general softmax function could deal whit any function for CPD, and could be applicable for any models. Simulation test proved that the effect of classification was fine.

  7. QuateXelero: An Accelerated Exact Network Motif Detection Algorithm

    PubMed Central

    Khakabimamaghani, Sahand; Sharafuddin, Iman; Dichter, Norbert; Koch, Ina; Masoudi-Nejad, Ali

    2013-01-01

    Finding motifs in biological, social, technological, and other types of networks has become a widespread method to gain more knowledge about these networks’ structure and function. However, this task is very computationally demanding, because it is highly associated with the graph isomorphism which is an NP problem (not known to belong to P or NP-complete subsets yet). Accordingly, this research is endeavoring to decrease the need to call NAUTY isomorphism detection method, which is the most time-consuming step in many existing algorithms. The work provides an extremely fast motif detection algorithm called QuateXelero, which has a Quaternary Tree data structure in the heart. The proposed algorithm is based on the well-known ESU (FANMOD) motif detection algorithm. The results of experiments on some standard model networks approve the overal superiority of the proposed algorithm, namely QuateXelero, compared with two of the fastest existing algorithms, G-Tries and Kavosh. QuateXelero is especially fastest in constructing the central data structure of the algorithm from scratch based on the input network. PMID:23874498

  8. A hybrid behavioural rule of adaptation and drift explains the emergent architecture of antagonistic networks

    PubMed Central

    Nuwagaba, S.; Zhang, F.; Hui, C.

    2015-01-01

    Ecological processes that can realistically account for network architectures are central to our understanding of how species assemble and function in ecosystems. Consumer species are constantly selecting and adjusting which resource species are to be exploited in an antagonistic network. Here we incorporate a hybrid behavioural rule of adaptive interaction switching and random drift into a bipartite network model. Predictions are insensitive to the model parameters and the initial network structures, and agree extremely well with the observed levels of modularity, nestedness and node-degree distributions for 61 real networks. Evolutionary and community assemblage histories only indirectly affect network structure by defining the size and complexity of ecological networks, whereas adaptive interaction switching and random drift carve out the details of network architecture at the faster ecological time scale. The hybrid behavioural rule of both adaptation and drift could well be the key processes for structure emergence in real ecological networks. PMID:25925104

  9. Datasets for radiation network algorithm development and testing

    SciTech Connect

    Rao, Nageswara S; Sen, Satyabrata; Berry, M. L..; Wu, Qishi; Grieme, M.; Brooks, Richard R; Cordone, G.

    2016-01-01

    Domestic Nuclear Detection Office s (DNDO) Intelligence Radiation Sensors Systems (IRSS) program supported the development of networks of commercial-off-the-shelf (COTS) radiation counters for detecting, localizing, and identifying low-level radiation sources. Under this program, a series of indoor and outdoor tests were conducted with multiple source strengths and types, different background profiles, and various types of source and detector movements. Following the tests, network algorithms were replayed in various re-constructed scenarios using sub-networks. These measurements and algorithm traces together provide a rich collection of highly valuable datasets for testing the current and next generation radiation network algorithms, including the ones (to be) developed by broader R&D communities such as distributed detection, information fusion, and sensor networks. From this multiple TeraByte IRSS database, we distilled out and packaged the first batch of canonical datasets for public release. They include measurements from ten indoor and two outdoor tests which represent increasingly challenging baseline scenarios for robustly testing radiation network algorithms.

  10. Transmission Scheduling and Routing Algorithms for Delay Tolerant Networks

    NASA Technical Reports Server (NTRS)

    Dudukovich, Rachel; Raible, Daniel E.

    2016-01-01

    The challenges of data processing, transmission scheduling and routing within a space network present a multi-criteria optimization problem. Long delays, intermittent connectivity, asymmetric data rates and potentially high error rates make traditional networking approaches unsuitable. The delay tolerant networking architecture and protocols attempt to mitigate many of these issues, yet transmission scheduling is largely manually configured and routes are determined by a static contact routing graph. A high level of variability exists among the requirements and environmental characteristics of different missions, some of which may allow for the use of more opportunistic routing methods. In all cases, resource allocation and constraints must be balanced with the optimization of data throughput and quality of service. Much work has been done researching routing techniques for terrestrial-based challenged networks in an attempt to optimize contact opportunities and resource usage. This paper examines several popular methods to determine their potential applicability to space networks.

  11. Network algorithms for information analysis using the Titan Toolkit.

    SciTech Connect

    McLendon, William Clarence, III; Baumes, Jeffrey; Wilson, Andrew T.; Wylie, Brian Neil; Shead, Timothy M.

    2010-07-01

    The analysis of networked activities is dramatically more challenging than many traditional kinds of analysis. A network is defined by a set of entities (people, organizations, banks, computers, etc.) linked by various types of relationships. These entities and relationships are often uninteresting alone, and only become significant in aggregate. The analysis and visualization of these networks is one of the driving factors behind the creation of the Titan Toolkit. Given the broad set of problem domains and the wide ranging databases in use by the information analysis community, the Titan Toolkit's flexible, component based pipeline provides an excellent platform for constructing specific combinations of network algorithms and visualizations.

  12. Modeling gene regulatory networks: A network simplification algorithm

    NASA Astrophysics Data System (ADS)

    Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.

    2016-12-01

    Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.

  13. Energy-Efficient Transmissions for Remote Wireless Sensor Networks: An Integrated HAP/Satellite Architecture for Emergency Scenarios.

    PubMed

    Dong, Feihong; Li, Hongjun; Gong, Xiangwu; Liu, Quan; Wang, Jingchao

    2015-09-03

    A typical application scenario of remote wireless sensor networks (WSNs) is identified as an emergency scenario. One of the greatest design challenges for communications in emergency scenarios is energy-efficient transmission, due to scarce electrical energy in large-scale natural and man-made disasters. Integrated high altitude platform (HAP)/satellite networks are expected to optimally meet emergency communication requirements. In this paper, a novel integrated HAP/satellite (IHS) architecture is proposed, and three segments of the architecture are investigated in detail. The concept of link-state advertisement (LSA) is designed in a slow flat Rician fading channel. The LSA is received and processed by the terminal to estimate the link state information, which can significantly reduce the energy consumption at the terminal end. Furthermore, the transmission power requirements of the HAPs and terminals are derived using the gradient descent and differential equation methods. The energy consumption is modeled at both the source and system level. An innovative and adaptive algorithm is given for the energy-efficient path selection. The simulation results validate the effectiveness of the proposed adaptive algorithm. It is shown that the proposed adaptive algorithm can significantly improve energy efficiency when combined with the LSA and the energy consumption estimation.

  14. Energy-Efficient Transmissions for Remote Wireless Sensor Networks: An Integrated HAP/Satellite Architecture for Emergency Scenarios

    PubMed Central

    Dong, Feihong; Li, Hongjun; Gong, Xiangwu; Liu, Quan; Wang, Jingchao

    2015-01-01

    A typical application scenario of remote wireless sensor networks (WSNs) is identified as an emergency scenario. One of the greatest design challenges for communications in emergency scenarios is energy-efficient transmission, due to scarce electrical energy in large-scale natural and man-made disasters. Integrated high altitude platform (HAP)/satellite networks are expected to optimally meet emergency communication requirements. In this paper, a novel integrated HAP/satellite (IHS) architecture is proposed, and three segments of the architecture are investigated in detail. The concept of link-state advertisement (LSA) is designed in a slow flat Rician fading channel. The LSA is received and processed by the terminal to estimate the link state information, which can significantly reduce the energy consumption at the terminal end. Furthermore, the transmission power requirements of the HAPs and terminals are derived using the gradient descent and differential equation methods. The energy consumption is modeled at both the source and system level. An innovative and adaptive algorithm is given for the energy-efficient path selection. The simulation results validate the effectiveness of the proposed adaptive algorithm. It is shown that the proposed adaptive algorithm can significantly improve energy efficiency when combined with the LSA and the energy consumption estimation. PMID:26404292

  15. Algorithm of Finding Hypo-Critical Path in Network Planning

    NASA Astrophysics Data System (ADS)

    Qi, Jianxun; Zhao, Xiuhua

    Network planning technology could be used to represent project plan management, such Critical Path Method (CPM for short) and Performance Evaluation Review Technique (PERT for short) etc. Aiming at problem that how to find hypo-critical path in network planning, firstly, properties of total float. free float and safety float are analyzed, and total float theorem is deduced on the basis of above analysis; and secondly, simple algorithm of finding the hypo-critical path is designed by using these properties of float and total theorem, and correctness of the algorithm is analyzed. Proof shows that the algorithm could realize effect of whole optimization could be realized by part optimization. Finally, one illustration is given to expatiate the algorithm.

  16. Speeding-up Bioinformatics Algorithms with Heterogeneous Architectures: Highly Heterogeneous Smith-Waterman (HHeterSW).

    PubMed

    Gálvez, Sergio; Ferusic, Adis; Esteban, Francisco J; Hernández, Pilar; Caballero, Juan A; Dorado, Gabriel

    2016-10-01

    The Smith-Waterman algorithm has a great sensitivity when used for biological sequence-database searches, but at the expense of high computing-power requirements. To overcome this problem, there are implementations in literature that exploit the different hardware-architectures available in a standard PC, such as GPU, CPU, and coprocessors. We introduce an application that splits the original database-search problem into smaller parts, resolves each of them by executing the most efficient implementations of the Smith-Waterman algorithms in different hardware architectures, and finally unifies the generated results. Using non-overlapping hardware allows simultaneous execution, and up to 2.58-fold performance gain, when compared with any other algorithm to search sequence databases. Even the performance of the popular BLAST heuristic is exceeded in 78% of the tests. The application has been tested with standard hardware: Intel i7-4820K CPU, Intel Xeon Phi 31S1P coprocessors, and nVidia GeForce GTX 960 graphics cards. An important increase in performance has been obtained in a wide range of situations, effectively exploiting the available hardware.

  17. Comparison and evaluation of network clustering algorithms applied to genetic interaction networks.

    PubMed

    Hou, Lin; Wang, Lin; Berg, Arthur; Qian, Minping; Zhu, Yunping; Li, Fangting; Deng, Minghua

    2012-01-01

    The goal of network clustering algorithms detect dense clusters in a network, and provide a first step towards the understanding of large scale biological networks. With numerous recent advances in biotechnologies, large-scale genetic interactions are widely available, but there is a limited understanding of which clustering algorithms may be most effective. In order to address this problem, we conducted a systematic study to compare and evaluate six clustering algorithms in analyzing genetic interaction networks, and investigated influencing factors in choosing algorithms. The algorithms considered in this comparison include hierarchical clustering, topological overlap matrix, bi-clustering, Markov clustering, Bayesian discriminant analysis based community detection, and variational Bayes approach to modularity. Both experimentally identified and synthetically constructed networks were used in this comparison. The accuracy of the algorithms is measured by the Jaccard index in comparing predicted gene modules with benchmark gene sets. The results suggest that the choice differs according to the network topology and evaluation criteria. Hierarchical clustering showed to be best at predicting protein complexes; Bayesian discriminant analysis based community detection proved best under epistatic miniarray profile (EMAP) datasets; the variational Bayes approach to modularity was noticeably better than the other algorithms in the genome-scale networks.

  18. Modeling of a 3DTV service in the software-defined networking architecture

    NASA Astrophysics Data System (ADS)

    Wilczewski, Grzegorz

    2014-11-01

    In this article a newly developed concept towards modeling of a multimedia service offering stereoscopic motion imagery is presented. Proposed model is based on the approach of utilization of Software-defined Networking or Software Defined Networks architecture (SDN). The definition of 3D television service spanning SDN concept is identified, exposing basic characteristic of a 3DTV service in a modern networking organization layout. Furthermore, exemplary functionalities of the proposed 3DTV model are depicted. It is indicated that modeling of a 3DTV service in the Software-defined Networking architecture leads to multiplicity of improvements, especially towards flexibility of a service supporting heterogeneity of end user devices.

  19. An optical circuit switching network architecture and reconfiguration schemes for datacenter

    NASA Astrophysics Data System (ADS)

    Zhou, Luying; Xu, Zhaowen; Cheng, Xiaofei; Huang, Qirui

    2015-01-01

    The paper presents an optical circuit switching network architecture and its control and traffic scheduling schemes. This optical switch network is built on a 32×32 AWG and fast wavelength tunable transmitters and is able to support up to 128 interface ports. The feasibility of the architecture is verified through an experimental study. The traffic scheduling scheme takes into account the transmitter wavelength tuning time so as to avoid frequent connection reconfiguration. The simulation results show that fast connection reconfiguration speed and efficient traffic scheduling scheme enable optical circuit switching network to deliver high transmission bandwidth and low packet delay performance in datacenter network.

  20. Quantum-based algorithm for optimizing artificial neural networks.

    PubMed

    Tzyy-Chyang Lu; Gwo-Ruey Yu; Jyh-Ching Juang

    2013-08-01

    This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms.

  1. Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

    NASA Astrophysics Data System (ADS)

    Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

    2015-10-01

    Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

  2. Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm

    PubMed Central

    Wu, Haizhou; Luo, Qifang

    2016-01-01

    Symbiotic organisms search (SOS) is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs). In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared. PMID:28105044

  3. Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

    PubMed Central

    Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.

    2014-01-01

    In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057

  4. Hybrid algorithms for fuzzy reverse supply chain network design.

    PubMed

    Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua

    2014-01-01

    In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.

  5. Behavioral Profiling of Scada Network Traffic Using Machine Learning Algorithms

    DTIC Science & Technology

    2014-03-27

    Conference on, 1–10. IEEE, 2011. [9] Cheung, S., B . Dutertre, M. Fong, U. Lindqvist, K. Skinner , and A. Valdes. “Using model-based intrusion detection for... BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS THESIS Jessica R. Werling, Captain, USAF AFIT-ENG-14-M-81 DEPARTMENT...subject to copyright protection in the United States. AFIT-ENG-14-M-81 BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING

  6. Space Network IP Services (SNIS): An Architecture for Supporting Low Earth Orbiting IP Satellite Missions

    NASA Technical Reports Server (NTRS)

    Israel, David J.

    2005-01-01

    The NASA Space Network (SN) supports a variety of missions using the Tracking and Data Relay Satellite System (TDRSS), which includes ground stations in White Sands, New Mexico and Guam. A Space Network IP Services (SNIS) architecture is being developed to support future users with requirements for end-to-end Internet Protocol (IP) communications. This architecture will support all IP protocols, including Mobile IP, over TDRSS Single Access, Multiple Access, and Demand Access Radio Frequency (RF) links. This paper will describe this architecture and how it can enable Low Earth Orbiting IP satellite missions.

  7. Architectural and operational considerations emerging from hybrid RF-optical network loading simulations

    NASA Astrophysics Data System (ADS)

    Chen, Yijiang; Abraham, Douglas S.; Heckman, David P.; Kwok, Andrew; MacNeal, Bruce E.; Tran, Kristy; Wu, Janet P.

    2016-03-01

    A technology demonstration of free space optical communication at interplanetary distances is planned via one or more future NASA deep-space missions. Such demonstrations will "pave the way" for operational use of optical communications on future robotic/potential Human missions. Hence, the Deep Space Network architecture will need to evolve. Preliminary attempts to model the anticipated future mission set and simulate how well it loads onto assumed architectures with combinations of RF and optical apertures have been evaluated. This paper discusses the results of preliminary loading simulations for hybrid RF-optical network architectures and highlights key mission and ground infrastructure considerations that emerge.

  8. AdaBoost-based algorithm for network intrusion detection.

    PubMed

    Hu, Weiming; Hu, Wei; Maybank, Steve

    2008-04-01

    Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for avoiding overfitting are adopted to improve the performance of the algorithm. Experimental results show that our algorithm has low computational complexity and error rates, as compared with algorithms of higher computational complexity, as tested on the benchmark sample data.

  9. Optical network architectures for dynamic reconfiguration of full duplex multiwavelength, radio over fiber

    NASA Astrophysics Data System (ADS)

    Attard, J. C.; Mitchell, J. E.

    2006-06-01

    In recent years there have been rapid advances in the techniques to generate and transport radio signals over optical fiber, but very little has been done to date to develop the concept of a full duplex, multiwavelength, radio-over-fiber network architecture that intrinsically supports and facilitates the dynamic reconfiguration of the wireless network. Recently the development of suitable architectures has received attention, with some approaches concentrating on the fiber radio link layer [Fiber Integr. Opt.19, 167 (2000)] and others focusing on the optical wavelength division multiplexing layer [L. Lim , in International Topical Meeting on Microwave Photonics: MWP'99 (IEEE, 1999), p. 127; also Fiber Opt. Business (15 November 2000)]. Here we present, for the first time to our knowledge, an approach that aims to vertically integrate the cellular radio layer, the fiber radio layer, the optical networking layer, and the physical layer to achieve a network architecture that enables the dynamic reconfiguration of the cellular wireless network layer.

  10. A neural network architecture for implementation of expert systems for real time monitoring

    NASA Technical Reports Server (NTRS)

    Ramamoorthy, P. A.

    1991-01-01

    Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

  11. A new mutually reinforcing network node and link ranking algorithm

    NASA Astrophysics Data System (ADS)

    Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E.

    2015-10-01

    This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity.

  12. Mitigate Cascading Failures on Networks using a Memetic Algorithm

    PubMed Central

    Tang, Xianglong; Liu, Jing; Hao, Xingxing

    2016-01-01

    Research concerning cascading failures in complex networks has become a hot topic. However, most of the existing studies have focused on modelling the cascading phenomenon on networks and analysing network robustness from a theoretical point of view, which considers only the damage incurred by the failure of one or several nodes. However, such a theoretical approach may not be useful in practical situation. Thus, we first design a much more practical measure to evaluate the robustness of networks against cascading failures, termed Rcf. Then, adopting Rcf as the objective function, we propose a new memetic algorithm (MA) named MA-Rcf to enhance network the robustness against cascading failures. Moreover, we design a new local search operator that considers the characteristics of cascading failures and operates by connecting nodes with a high probability of having similar loads. In experiments, both synthetic scale-free networks and real-world networks are used to test the efficiency and effectiveness of the MA-Rcf. We systematically investigate the effects of parameters on the performance of the MA-Rcf and validate the performance of the newly designed local search operator. The results show that the local search operator is effective, that MA-Rcf can enhance network robustness against cascading failures efficiently, and that it outperforms existing algorithms. PMID:27934964

  13. Mitigate Cascading Failures on Networks using a Memetic Algorithm.

    PubMed

    Tang, Xianglong; Liu, Jing; Hao, Xingxing

    2016-12-09

    Research concerning cascading failures in complex networks has become a hot topic. However, most of the existing studies have focused on modelling the cascading phenomenon on networks and analysing network robustness from a theoretical point of view, which considers only the damage incurred by the failure of one or several nodes. However, such a theoretical approach may not be useful in practical situation. Thus, we first design a much more practical measure to evaluate the robustness of networks against cascading failures, termed Rcf. Then, adopting Rcf as the objective function, we propose a new memetic algorithm (MA) named MA-Rcf to enhance network the robustness against cascading failures. Moreover, we design a new local search operator that considers the characteristics of cascading failures and operates by connecting nodes with a high probability of having similar loads. In experiments, both synthetic scale-free networks and real-world networks are used to test the efficiency and effectiveness of the MA-Rcf. We systematically investigate the effects of parameters on the performance of the MA-Rcf and validate the performance of the newly designed local search operator. The results show that the local search operator is effective, that MA-Rcf can enhance network robustness against cascading failures efficiently, and that it outperforms existing algorithms.

  14. Mitigate Cascading Failures on Networks using a Memetic Algorithm

    NASA Astrophysics Data System (ADS)

    Tang, Xianglong; Liu, Jing; Hao, Xingxing

    2016-12-01

    Research concerning cascading failures in complex networks has become a hot topic. However, most of the existing studies have focused on modelling the cascading phenomenon on networks and analysing network robustness from a theoretical point of view, which considers only the damage incurred by the failure of one or several nodes. However, such a theoretical approach may not be useful in practical situation. Thus, we first design a much more practical measure to evaluate the robustness of networks against cascading failures, termed Rcf. Then, adopting Rcf as the objective function, we propose a new memetic algorithm (MA) named MA-Rcf to enhance network the robustness against cascading failures. Moreover, we design a new local search operator that considers the characteristics of cascading failures and operates by connecting nodes with a high probability of having similar loads. In experiments, both synthetic scale-free networks and real-world networks are used to test the efficiency and effectiveness of the MA-Rcf. We systematically investigate the effects of parameters on the performance of the MA-Rcf and validate the performance of the newly designed local search operator. The results show that the local search operator is effective, that MA-Rcf can enhance network robustness against cascading failures efficiently, and that it outperforms existing algorithms.

  15. A new mutually reinforcing network node and link ranking algorithm

    PubMed Central

    Wang, Zhenghua; Dueñas-Osorio, Leonardo; Padgett, Jamie E.

    2015-01-01

    This study proposes a novel Normalized Wide network Ranking algorithm (NWRank) that has the advantage of ranking nodes and links of a network simultaneously. This algorithm combines the mutual reinforcement feature of Hypertext Induced Topic Selection (HITS) and the weight normalization feature of PageRank. Relative weights are assigned to links based on the degree of the adjacent neighbors and the Betweenness Centrality instead of assigning the same weight to every link as assumed in PageRank. Numerical experiment results show that NWRank performs consistently better than HITS, PageRank, eigenvector centrality, and edge betweenness from the perspective of network connectivity and approximate network flow, which is also supported by comparisons with the expensive N-1 benchmark removal criteria based on network efficiency. Furthermore, it can avoid some problems, such as the Tightly Knit Community effect, which exists in HITS. NWRank provides a new inexpensive way to rank nodes and links of a network, which has practical applications, particularly to prioritize resource allocation for upgrade of hierarchical and distributed networks, as well as to support decision making in the design of networks, where node and link importance depend on a balance of local and global integrity. PMID:26492958

  16. The density matrix renormalization group algorithm on kilo-processor architectures: Implementation and trade-offs

    NASA Astrophysics Data System (ADS)

    Nemes, Csaba; Barcza, Gergely; Nagy, Zoltán; Legeza, Örs; Szolgay, Péter

    2014-06-01

    In the numerical analysis of strongly correlated quantum lattice models one of the leading algorithms developed to balance the size of the effective Hilbert space and the accuracy of the simulation is the density matrix renormalization group (DMRG) algorithm, in which the run-time is dominated by the iterative diagonalization of the Hamilton operator. As the most time-dominant step of the diagonalization can be expressed as a list of dense matrix operations, the DMRG is an appealing candidate to fully utilize the computing power residing in novel kilo-processor architectures. In the paper a smart hybrid CPU-GPU implementation is presented, which exploits the power of both CPU and GPU and tolerates problems exceeding the GPU memory size. Furthermore, a new CUDA kernel has been designed for asymmetric matrix-vector multiplication to accelerate the rest of the diagonalization. Besides the evaluation of the GPU implementation, the practical limits of an FPGA implementation are also discussed.

  17. Computer Algorithms and Architectures for Three-Dimensional Eddy-Current Nondestructive Evaluation. Volume 3. Chapters 6-11

    DTIC Science & Technology

    1989-01-20

    mflC FILE. OOR SA/TR-2/89 A003: FINAL REPORT COMPUTER ALGORITHMS AND ARCHITECTURES FOR THREE-DIMENSIONAL EDDY-CURRENT NONDESTRUCTIVE EVALUATION CD...J., Ullman, J., The Design and Analysis of Computer Algorithms , Addison-Wesley Publishing Company, 1974. [A2] Anderson, B., Moore, J., Optimal...actual data. DC- 17 I I I I [All Aho, A., Hopcroft, J., Ullman, J., The Design and Analysis of Computer Algorithms , Addison-Wesley Publishing Company

  18. Architecture and System Engineering Development Study of Space-Based Satellite Networks for NASA Missions

    NASA Technical Reports Server (NTRS)

    Ivancic, William D.

    2003-01-01

    Traditional NASA missions, both near Earth and deep space, have been stovepipe in nature and point-to-point in architecture. Recently, NASA and others have conceptualized missions that required space-based networking. The notion of networks in space is a drastic shift in thinking and requires entirely new architectures, radio systems (antennas, modems, and media access), and possibly even new protocols. A full system engineering approach for some key mission architectures will occur that considers issues such as the science being performed, stationkeeping, antenna size, contact time, data rates, radio-link power requirements, media access techniques, and appropriate networking and transport protocols. This report highlights preliminary architecture concepts and key technologies that will be investigated.

  19. Estimation of Distribution Algorithm for Resource Allocation in Green Cooperative Cognitive Radio Sensor Networks

    PubMed Central

    Naeem, Muhammad; Pareek, Udit; Lee, Daniel C.; Anpalagan, Alagan

    2013-01-01

    Due to the rapid increase in the usage and demand of wireless sensor networks (WSN), the limited frequency spectrum available for WSN applications will be extremely crowded in the near future. More sensor devices also mean more recharging/replacement of batteries, which will cause significant impact on the global carbon footprint. In this paper, we propose a relay-assisted cognitive radio sensor network (CRSN) that allocates communication resources in an environmentally friendly manner. We use shared band amplify and forward relaying for cooperative communication in the proposed CRSN. We present a multi-objective optimization architecture for resource allocation in a green cooperative cognitive radio sensor network (GC-CRSN). The proposed multi-objective framework jointly performs relay assignment and power allocation in GC-CRSN, while optimizing two conflicting objectives. The first objective is to maximize the total throughput, and the second objective is to minimize the total transmission power of CRSN. The proposed relay assignment and power allocation problem is a non-convex mixed-integer non-linear optimization problem (NC-MINLP), which is generally non-deterministic polynomial-time (NP)-hard. We introduce a hybrid heuristic algorithm for this problem. The hybrid heuristic includes an estimation-of-distribution algorithm (EDA) for performing power allocation and iterative greedy schemes for constraint satisfaction and relay assignment. We analyze the throughput and power consumption tradeoff in GC-CRSN. A detailed analysis of the performance of the proposed algorithm is presented with the simulation results. PMID:23584119

  20. Research on Critical Nodes Algorithm in Social Complex Networks

    NASA Astrophysics Data System (ADS)

    Wang, Xue-Guang

    2017-01-01

    Discovering critical nodes in social networks has many important applications and has attracted more and more institutions and scholars. How to determine the K critical nodes with the most influence in a social network is a NP (define) problem. Considering the widespread community structure, this paper presents an algorithm for discovering critical nodes based on two information diffusion models and obtains each node's marginal contribution by using a Monte-Carlo method in social networks. The solution of the critical nodes problem is the K nodes with the highest marginal contributions. The feasibility and effectiveness of our method have been verified on two synthetic datasets and four real datasets.

  1. An Algorithm for the Mixed Transportation Network Design Problem.

    PubMed

    Liu, Xinyu; Chen, Qun

    2016-01-01

    This paper proposes an optimization algorithm, the dimension-down iterative algorithm (DDIA), for solving a mixed transportation network design problem (MNDP), which is generally expressed as a mathematical programming with equilibrium constraint (MPEC). The upper level of the MNDP aims to optimize the network performance via both the expansion of the existing links and the addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium (UE) problem. The idea of the proposed solution algorithm (DDIA) is to reduce the dimensions of the problem. A group of variables (discrete/continuous) is fixed to optimize another group of variables (continuous/discrete) alternately; then, the problem is transformed into solving a series of CNDPs (continuous network design problems) and DNDPs (discrete network design problems) repeatedly until the problem converges to the optimal solution. The advantage of the proposed algorithm is that its solution process is very simple and easy to apply. Numerical examples show that for the MNDP without budget constraint, the optimal solution can be found within a few iterations with DDIA. For the MNDP with budget constraint, however, the result depends on the selection of initial values, which leads to different optimal solutions (i.e., different local optimal solutions). Some thoughts are given on how to derive meaningful initial values, such as by considering the budgets of new and reconstruction projects separately.

  2. An Algorithm for the Mixed Transportation Network Design Problem

    PubMed Central

    Liu, Xinyu; Chen, Qun

    2016-01-01

    This paper proposes an optimization algorithm, the dimension-down iterative algorithm (DDIA), for solving a mixed transportation network design problem (MNDP), which is generally expressed as a mathematical programming with equilibrium constraint (MPEC). The upper level of the MNDP aims to optimize the network performance via both the expansion of the existing links and the addition of new candidate links, whereas the lower level is a traditional Wardrop user equilibrium (UE) problem. The idea of the proposed solution algorithm (DDIA) is to reduce the dimensions of the problem. A group of variables (discrete/continuous) is fixed to optimize another group of variables (continuous/discrete) alternately; then, the problem is transformed into solving a series of CNDPs (continuous network design problems) and DNDPs (discrete network design problems) repeatedly until the problem converges to the optimal solution. The advantage of the proposed algorithm is that its solution process is very simple and easy to apply. Numerical examples show that for the MNDP without budget constraint, the optimal solution can be found within a few iterations with DDIA. For the MNDP with budget constraint, however, the result depends on the selection of initial values, which leads to different optimal solutions (i.e., different local optimal solutions). Some thoughts are given on how to derive meaningful initial values, such as by considering the budgets of new and reconstruction projects separately. PMID:27626803

  3. Adaptive Fault Detection on Liquid Propulsion Systems with Virtual Sensors: Algorithms and Architectures

    NASA Technical Reports Server (NTRS)

    Matthews, Bryan L.; Srivastava, Ashok N.

    2010-01-01

    Prior to the launch of STS-119 NASA had completed a study of an issue in the flow control valve (FCV) in the Main Propulsion System of the Space Shuttle using an adaptive learning method known as Virtual Sensors. Virtual Sensors are a class of algorithms that estimate the value of a time series given other potentially nonlinearly correlated sensor readings. In the case presented here, the Virtual Sensors algorithm is based on an ensemble learning approach and takes sensor readings and control signals as input to estimate the pressure in a subsystem of the Main Propulsion System. Our results indicate that this method can detect faults in the FCV at the time when they occur. We use the standard deviation of the predictions of the ensemble as a measure of uncertainty in the estimate. This uncertainty estimate was crucial to understanding the nature and magnitude of transient characteristics during startup of the engine. This paper overviews the Virtual Sensors algorithm and discusses results on a comprehensive set of Shuttle missions and also discusses the architecture necessary for deploying such algorithms in a real-time, closed-loop system or a human-in-the-loop monitoring system. These results were presented at a Flight Readiness Review of the Space Shuttle in early 2009.

  4. An Efficient Circulant MIMO Equalizer for CDMA Downlink: Algorithm and VLSI Architecture

    NASA Astrophysics Data System (ADS)

    Guo, Yuanbin; Zhang, Jianzhong(Charlie); McCain, Dennis; Cavallaro, Joseph R.

    2006-12-01

    We present an efficient circulant approximation-based MIMO equalizer architecture for the CDMA downlink. This reduces the direct matrix inverse (DMI) of size[InlineEquation not available: see fulltext.] with[InlineEquation not available: see fulltext.] complexity to some FFT operations with[InlineEquation not available: see fulltext.] complexity and the inverse of some[InlineEquation not available: see fulltext.] submatrices. We then propose parallel and pipelined VLSI architectures with Hermitian optimization and reduced-state FFT for further complexity optimization. Generic VLSI architectures are derived for the[InlineEquation not available: see fulltext.] high-order receiver from partitioned[InlineEquation not available: see fulltext.] submatrices. This leads to more parallel VLSI design with[InlineEquation not available: see fulltext.] further complexity reduction. Comparative study with both the conjugate-gradient and DMI algorithms shows very promising performance/complexity tradeoff. VLSI design space in terms of area/time efficiency is explored extensively for layered parallelism and pipelining with a Catapult C high-level-synthesis methodology.

  5. A novel algorithm and its VLSI architecture for connected component labeling

    NASA Astrophysics Data System (ADS)

    Zhao, Hualong; Sang, Hongshi; Zhang, Tianxu

    2011-11-01

    A novel line-based streaming labeling algorithm with its VLSI architecture is proposed in this paper. Line-based neighborhood examination scheme is used for efficient local connected components extraction. A novel reversed rooted tree hook-up strategy, which is very suitable for hardware implementation, is applied on the mergence stage of equivalent connected components. The reversed rooted tree hook-up strategy significant reduces the requirement of on-chip memory, which makes the chip area smaller. Clock domains crossing FIFOs are also applied for connecting the label core and external memory interface, which makes the label engine working in a higher frequency and raises the throughput of the label engine. Several performance tests have been performed for our proposed hardware implementation. The processing bandwidth of our hardware architecture can reach the I/O transfer boundary according to the external interface clock in all the real image tests. Beside the advantage of reducing the processing time, our hardware implementation can support the image size as large as 4096*4096, which will be very appealing in remote sensing or any other high-resolution image applications. The implementation of proposed architecture is synthesized with SMIC 180nm standard cell library. The work frequency of the label engine reaches 200MHz.

  6. GiPSiNet: an open source/open architecture network middleware for surgical simulations.

    PubMed

    Liberatore, Vincenzo; Cavuşoğlu, M Cenk; Cai, Qingbo

    2006-01-01

    In this paper, we present the design and techniques of GiPSiNet, an open source/open architecture network middleware being developed for surgical simulations. GiPSiNet extends GiPSi (General Interactive Physical Simulation Interface), our framework for developing organ level surgical simulations, to network environments. This network extension is non-trivial, since the network settings pose several serious problems for distributed surgical virtual environments such as band-width limit, delays, and packet losses. Our goal is to enhance the quality (fidelity and realism) of networked simulations in the absence of network QoS (Quality of Service) through the GiPSiNet middleware.

  7. Routing and wavelength allocation algorithm based on the weighted attack probability in software-defined optical networks

    NASA Astrophysics Data System (ADS)

    Zhao, Yongli; Zhang, Jie

    2017-02-01

    A routing and wavelength assignment (RWA) algorithm against high-power jamming based on software-defined optical networks (SDONs) is proposed. The SDON architecture is designed with power monitors at each node, which can collect the abnormal power information from each port and wavelength. Based on the abnormal power information, a metric, the weighted attack probability (WAP), can be calculated. A WAP-based RWA algorithm (WAP-RWA) is proposed considering the WAP values of each link and node along the selected lightpath. Numerical results show that the WAP-RWA algorithm can achieve a better performance in terms of blocking probability and resource utilization compared with the attack-aware dedicated path protection (AA-DPP) RWA (AA-DPP-RWA) algorithm, while providing a protection comparable with the AA-DPP-RWA algorithm.

  8. XSTREAM: A practical algorithm for identification and architecture modeling of tandem repeats in protein sequences

    PubMed Central

    Newman, Aaron M; Cooper, James B

    2007-01-01

    Background Biological sequence repeats arranged in tandem patterns are widespread in DNA and proteins. While many software tools have been designed to detect DNA tandem repeats (TRs), useful algorithms for identifying protein TRs with varied levels of degeneracy are still needed. Results To address limitations of current repeat identification methods, and to provide an efficient and flexible algorithm for the detection and analysis of TRs in protein sequences, we designed and implemented a new computational method called XSTREAM. Running time tests confirm the practicality of XSTREAM for analyses of multi-genome datasets. Each of the key capabilities of XSTREAM (e.g., merging, nesting, long-period detection, and TR architecture modeling) are demonstrated using anecdotal examples, and the utility of XSTREAM for identifying TR proteins was validated using data from a recently published paper. Conclusion We show that XSTREAM is a practical and valuable tool for TR detection in protein and nucleotide sequences at the multi-genome scale, and an effective tool for modeling TR domains with diverse architectures and varied levels of degeneracy. Because of these useful features, XSTREAM has significant potential for the discovery of naturally-evolved modular proteins with applications for engineering novel biostructural and biomimetic materials, and identifying new vaccine and diagnostic targets. PMID:17931424

  9. Information dynamics algorithm for detecting communities in networks

    NASA Astrophysics Data System (ADS)

    Massaro, Emanuele; Bagnoli, Franco; Guazzini, Andrea; Lió, Pietro

    2012-11-01

    The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network-inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method [4] by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.

  10. Resistive Network Optimal Power Flow: Uniqueness and Algorithms

    SciTech Connect

    Tan, CW; Cai, DWH; Lou, X

    2015-01-01

    The optimal power flow (OPF) problem minimizes the power loss in an electrical network by optimizing the voltage and power delivered at the network buses, and is a nonconvex problem that is generally hard to solve. By leveraging a recent development on the zero duality gap of OPF, we propose a second-order cone programming convex relaxation of the resistive network OPF, and study the uniqueness of the optimal solution using differential topology, especially the Poincare-Hopf Index Theorem. We characterize the global uniqueness for different network topologies, e.g., line, radial, and mesh networks. This serves as a starting point to design distributed local algorithms with global behaviors that have low complexity, are computationally fast, and can run under synchronous and asynchronous settings in practical power grids.

  11. 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.

  12. Genetic Algorithm Application in Optimization of Wireless Sensor Networks

    PubMed Central

    Norouzi, Ali; Zaim, A. Halim

    2014-01-01

    There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. PMID:24693235

  13. Voronoi-based localisation algorithm for mobile sensor networks

    NASA Astrophysics Data System (ADS)

    Guan, Zixiao; Zhang, Yongtao; Zhang, Baihai; Dong, Lijing

    2016-11-01

    Localisation is an essential and important part in wireless sensor networks (WSNs). Many applications require location information. So far, there are less researchers studying on mobile sensor networks (MSNs) than static sensor networks (SSNs). However, MSNs are required in more and more areas such that the number of anchor nodes can be reduced and the location accuracy can be improved. In this paper, we firstly propose a range-free Voronoi-based Monte Carlo localisation algorithm (VMCL) for MSNs. We improve the localisation accuracy by making better use of the information that a sensor node gathers. Then, we propose an optimal region selection strategy of Voronoi diagram based on VMCL, called ORSS-VMCL, to increase the efficiency and accuracy for VMCL by adapting the size of Voronoi area during the filtering process. Simulation results show that the accuracy of these two algorithms, especially ORSS-VMCL, outperforms traditional MCL.

  14. A Flexible Reservation Algorithm for Advance Network Provisioning

    SciTech Connect

    Balman, Mehmet; Chaniotakis, Evangelos; Shoshani, Arie; Sim, Alex

    2010-04-12

    Many scientific applications need support from a communication infrastructure that provides predictable performance, which requires effective algorithms for bandwidth reservations. Network reservation systems such as ESnet's OSCARS, establish guaranteed bandwidth of secure virtual circuits for a certain bandwidth and length of time. However, users currently cannot inquire about bandwidth availability, nor have alternative suggestions when reservation requests fail. In general, the number of reservation options is exponential with the number of nodes n, and current reservation commitments. We present a novel approach for path finding in time-dependent networks taking advantage of user-provided parameters of total volume and time constraints, which produces options for earliest completion and shortest duration. The theoretical complexity is only O(n2r2) in the worst-case, where r is the number of reservations in the desired time interval. We have implemented our algorithm and developed efficient methodologies for incorporation into network reservation frameworks. Performance measurements confirm the theoretical predictions.

  15. The No-Prop algorithm: a new learning algorithm for multilayer neural networks.

    PubMed

    Widrow, Bernard; Greenblatt, Aaron; Kim, Youngsik; Park, Dookun

    2013-01-01

    A new learning algorithm for multilayer neural networks that we have named No-Propagation (No-Prop) is hereby introduced. With this algorithm, the weights of the hidden-layer neurons are set and fixed with random values. Only the weights of the output-layer neurons are trained, using steepest descent to minimize mean square error, with the LMS algorithm of Widrow and Hoff. The purpose of introducing nonlinearity with the hidden layers is examined from the point of view of Least Mean Square Error Capacity (LMS Capacity), which is defined as the maximum number of distinct patterns that can be trained into the network with zero error. This is shown to be equal to the number of weights of each of the output-layer neurons. The No-Prop algorithm and the Back-Prop algorithm are compared. Our experience with No-Prop is limited, but from the several examples presented here, it seems that the performance regarding training and generalization of both algorithms is essentially the same when the number of training patterns is less than or equal to LMS Capacity. When the number of training patterns exceeds Capacity, Back-Prop is generally the better performer. But equivalent performance can be obtained with No-Prop by increasing the network Capacity by increasing the number of neurons in the hidden layer that drives the output layer. The No-Prop algorithm is much simpler and easier to implement than Back-Prop. Also, it converges much faster. It is too early to definitively say where to use one or the other of these algorithms. This is still a work in progress.

  16. Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn.

    PubMed

    Patra, Tarak K; Meenakshisundaram, Venkatesh; Hung, Jui-Hsiang; Simmons, David S

    2017-02-13

    Machine learning has the potential to dramatically accelerate high-throughput approaches to materials design, as demonstrated by successes in biomolecular design and hard materials design. However, in the search for new soft materials exhibiting properties and performance beyond those previously achieved, machine learning approaches are frequently limited by two shortcomings. First, because they are intrinsically interpolative, they are better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require large pre-existing data sets, which are frequently unavailable and prohibitively expensive to produce. Here we describe a new strategy, the neural-network-biased genetic algorithm (NBGA), for combining genetic algorithms, machine learning, and high-throughput computation or experiment to discover materials with extremal properties in the absence of pre-existing data. Within this strategy, predictions from a progressively constructed artificial neural network are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct simulation or experiment. In effect, this strategy gives the evolutionary algorithm the ability to "learn" and draw inferences from its experience to accelerate the evolutionary process. We test this algorithm against several standard optimization problems and polymer design problems and demonstrate that it matches and typically exceeds the efficiency and reproducibility of standard approaches including a direct-evaluation genetic algorithm and a neural-network-evaluated genetic algorithm. The success of this algorithm in a range of test problems indicates that the NBGA provides a robust strategy for employing informatics-accelerated high-throughput methods to accelerate materials design in the absence of pre-existing data.

  17. A network architecture for International Business Satellite communications

    NASA Astrophysics Data System (ADS)

    Takahata, Fumio; Nohara, Mitsuo; Takeuchi, Yoshio

    Demand Assignment (DA) control is expected to be introduced in the International Business Satellte communications (IBS) network in order to cope with a growing international business traffic. The paper discusses the DA/IBS network from the viewpoints of network configuration, satellite channel configuration and DA control. The network configuration proposed here consists of one Central Station with network management function and several Network Coordination Stations with user management function. A satellite channel configuration is also presented along with a tradeoff study on transmission bit rate, high power amplifier output power requirement, and service quality. The DA control flow and protocol based on CCITT Signalling System No. 7 are also proposed.

  18. Content-Based Multi-Channel Network Coding Algorithm in the Millimeter-Wave Sensor Network

    PubMed Central

    Lin, Kai; Wang, Di; Hu, Long

    2016-01-01

    With the development of wireless technology, the widespread use of 5G is already an irreversible trend, and millimeter-wave sensor networks are becoming more and more common. However, due to the high degree of complexity and bandwidth bottlenecks, the millimeter-wave sensor network still faces numerous problems. In this paper, we propose a novel content-based multi-channel network coding algorithm, which uses the functions of data fusion, multi-channel and network coding to improve the data transmission; the algorithm is referred to as content-based multi-channel network coding (CMNC). The CMNC algorithm provides a fusion-driven model based on the Dempster-Shafer (D-S) evidence theory to classify the sensor nodes into different classes according to the data content. By using the result of the classification, the CMNC algorithm also provides the channel assignment strategy and uses network coding to further improve the quality of data transmission in the millimeter-wave sensor network. Extensive simulations are carried out and compared to other methods. Our simulation results show that the proposed CMNC algorithm can effectively improve the quality of data transmission and has better performance than the compared methods. PMID:27376302

  19. Single-Iteration Learning Algorithm for Feed-Forward Neural Networks

    SciTech Connect

    Barhen, J.; Cogswell, R.; Protopopescu, V.

    1999-07-31

    A new methodology for neural learning is presented, whereby only a single iteration is required to train a feed-forward network with near-optimal results. To this aim, a virtual input layer is added to the multi-layer architecture. The virtual input layer is connected to the nominal input layer by a specird nonlinear transfer function, and to the fwst hidden layer by regular (linear) synapses. A sequence of alternating direction singular vrdue decompositions is then used to determine precisely the inter-layer synaptic weights. This algorithm exploits the known separability of the linear (inter-layer propagation) and nonlinear (neuron activation) aspects of information &ansfer within a neural network.

  20. Supplier selection based on a neural network model using genetic algorithm.

    PubMed

    Golmohammadi, Davood; Creese, Robert C; Valian, Haleh; Kolassa, John

    2009-09-01

    In this paper, a decision-making model was developed to select suppliers using neural networks (NNs). This model used historical supplier performance data for selection of vendor suppliers. Input and output were designed in a unique manner for training purposes. The managers' judgments about suppliers were simulated by using a pairwise comparisons matrix for output estimation in the NN. To obtain the benefit of a search technique for model structure and training, genetic algorithm (GA) was applied for the initial weights and architecture of the network. The suppliers' database information (input) can be updated over time to change the suppliers' score estimation based on their performance. The case study illustrated shows how the model can be applied for suppliers' selection.

  1. SANDS: a service-oriented architecture for clinical decision support in a National Health Information Network.

    PubMed

    Wright, Adam; Sittig, Dean F

    2008-12-01

    In this paper, we describe and evaluate a new distributed architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support), which leverages current health information exchange efforts and is based on the principles of a service-oriented architecture. The architecture allows disparate clinical information systems and clinical decision support systems to be seamlessly integrated over a network according to a set of interfaces and protocols described in this paper. The architecture described is fully defined and developed, and six use cases have been developed and tested using a prototype electronic health record which links to one of the existing prototype National Health Information Networks (NHIN): drug interaction checking, syndromic surveillance, diagnostic decision support, inappropriate prescribing in older adults, information at the point of care and a simple personal health record. Some of these use cases utilize existing decision support systems, which are either commercially or freely available at present, and developed outside of the SANDS project, while other use cases are based on decision support systems developed specifically for the project. Open source code for many of these components is available, and an open source reference parser is also available for comparison and testing of other clinical information systems and clinical decision support systems that wish to implement the SANDS architecture. The SANDS architecture for decision support has several significant advantages over other architectures for clinical decision support. The most salient of these are:

  2. Adaptive Load-Balancing Algorithms using Symmetric Broadcast Networks

    NASA Technical Reports Server (NTRS)

    Das, Sajal K.; Harvey, Daniel J.; Biswas, Rupak; Biegel, Bryan A. (Technical Monitor)

    2002-01-01

    In a distributed computing environment, it is important to ensure that the processor workloads are adequately balanced, Among numerous load-balancing algorithms, a unique approach due to Das and Prasad defines a symmetric broadcast network (SBN) that provides a robust communication pattern among the processors in a topology-independent manner. In this paper, we propose and analyze three efficient SBN-based dynamic load-balancing algorithms, and implement them on an SGI Origin2000. A thorough experimental study with Poisson distributed synthetic loads demonstrates that our algorithms are effective in balancing system load. By optimizing completion time and idle time, the proposed algorithms are shown to compare favorably with several existing approaches.

  3. New packet scheduling algorithm in wireless CDMA data networks

    NASA Astrophysics Data System (ADS)

    Wang, Yu; Gao, Zhuo; Li, Shaoqian; Li, Lemin

    2002-08-01

    The future 3G/4G wireless communication systems will provide internet access for mobile users. Packet scheduling algorithms are essential for QoS of diversified data traffics and efficient utilization of radio spectrum.This paper firstly presents a new packet scheduling algorithm DSTTF under the assumption of continuous transmission rates and scheduling intervals for CDMA data networks . Then considering the constraints of discrete transmission rates and fixed scheduling intervals imposed by the practical system, P-DSTTF, a modified version of DSTTF, is brought forward. Both scheduling algorithms take into consideration of channel condition, packet size and traffic delay bounds. The extensive simulation results demonstrate that the proposed scheduling algorithms are superior to some typical ones in current research. In addition, both static and dynamic wireless channel model of multi-level link capacity are established. These channel models sketch better the characterizations of wireless channel than two state Markov model widely adopted by the current literature.

  4. A Message-Passing Algorithm for Wireless Network Scheduling.

    PubMed

    Paschalidis, Ioannis Ch; Huang, Fuzhuo; Lai, Wei

    2015-10-01

    We consider scheduling in wireless networks and formulate it as Maximum Weighted Independent Set (MWIS) problem on a "conflict" graph that captures interference among simultaneous transmissions. We propose a novel, low-complexity, and fully distributed algorithm that yields high-quality feasible solutions. Our proposed algorithm consists of two phases, each of which requires only local information and is based on message-passing. The first phase solves a relaxation of the MWIS problem using a gradient projection method. The relaxation we consider is tighter than the simple linear programming relaxation and incorporates constraints on all cliques in the graph. The second phase of the algorithm starts from the solution of the relaxation and constructs a feasible solution to the MWIS problem. We show that our algorithm always outputs an optimal solution to the MWIS problem for perfect graphs. Simulation results compare our policies against Carrier Sense Multiple Access (CSMA) and other alternatives and show excellent performance.

  5. Evolutionary algorithms applied to reliable communication network design

    NASA Astrophysics Data System (ADS)

    Nesmachnow, Sergio; Cancela, Hector; Alba, Enrique

    2007-10-01

    Several evolutionary algorithms (EAs) applied to a wide class of communication network design problems modelled under the generalized Steiner problem (GSP) are evaluated. In order to provide a fault-tolerant design, a solution to this problem consists of a preset number of independent paths linking each pair of potentially communicating terminal nodes. This usually requires considering intermediate non-terminal nodes (Steiner nodes), which are used to ensure path redundancy, while trying to minimize the overall cost. The GSP is an NP-hard problem for which few algorithms have been proposed. This article presents a comparative study of pure and hybrid EAs applied to the GSP, codified over MALLBA, a general purpose library for combinatorial optimization. The algorithms were tested on several GSPs, and asset efficient numerical results are reported for both serial and distributed models of the evaluated algorithms.

  6. Design mobile satellite system architecture as an integral part of the cellular access digital network

    NASA Technical Reports Server (NTRS)

    Chien, E. S. K.; Marinho, J. A.; Russell, J. E., Sr.

    1988-01-01

    The Cellular Access Digital Network (CADN) is the access vehicle through which cellular technology is brought into the mainstream of the evolving integrated telecommunications network. Beyond the integrated end-to-end digital access and per call network services provisioning of the Integrated Services Digital Network (ISDN), the CADN engenders the added capability of mobility freedom via wireless access. One key element of the CADN network architecture is the standard user to network interface that is independent of RF transmission technology. Since the Mobile Satellite System (MSS) is envisioned to not only complement but also enhance the capabilities of the terrestrial cellular telecommunications network, compatibility and interoperability between terrestrial cellular and mobile satellite systems are vitally important to provide an integrated moving telecommunications network of the future. From a network standpoint, there exist very strong commonalities between the terrestrial cellular system and the mobile satellite system. Therefore, the MSS architecture should be designed as an integral part of the CADN. This paper describes the concept of the CADN, the functional architecture of the MSS, and the user-network interface signaling protocols.

  7. Metaheuristic Algorithms for Convolution Neural Network

    PubMed Central

    Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

    2016-01-01

    A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent). PMID:27375738

  8. Consensus algorithm in smart grid and communication networks

    NASA Astrophysics Data System (ADS)

    Alfagee, Husain Abdulaziz

    On a daily basis, consensus theory attracts more and more researches from different areas of interest, to apply its techniques to solve technical problems in a way that is faster, more reliable, and even more precise than ever before. A power system network is one of those fields that consensus theory employs extensively. The use of the consensus algorithm to solve the Economic Dispatch and Load Restoration Problems is a good example. Instead of a conventional central controller, some researchers have explored an algorithm to solve the above mentioned problems, in a distribution manner, using the consensus algorithm, which is based on calculation methods, i.e., non estimation methods, for updating the information consensus matrix. Starting from this point of solving these types of problems mentioned, specifically, in a distribution fashion, using the consensus algorithm, we have implemented a new advanced consensus algorithm. It is based on the adaptive estimation techniques, such as the Gradient Algorithm and the Recursive Least Square Algorithm, to solve the same problems. This advanced work was tested on different case studies that had formerly been explored, as seen in references 5, 7, and 18. Three and five generators, or agents, with different topologies, correspond to the Economic Dispatch Problem and the IEEE 16-Bus power system corresponds to the Load Restoration Problem. In all the cases we have studied, the results met our expectations with extreme accuracy, and completely matched the results of the previous researchers. There is little question that this research proves the capability and dependability of using the consensus algorithm, based on the estimation methods as the Gradient Algorithm and the Recursive Least Square Algorithm to solve such power problems.

  9. Control of Boolean networks: hardness results and algorithms for tree structured networks.

    PubMed

    Akutsu, Tatsuya; Hayashida, Morihiro; Ching, Wai-Ki; Ng, Michael K

    2007-02-21

    Finding control strategies of cells is a challenging and important problem in the post-genomic era. This paper considers theoretical aspects of the control problem using the Boolean network (BN), which is a simplified model of genetic networks. It is shown that finding a control strategy leading to the desired global state is computationally intractable (NP-hard) in general. Furthermore, this hardness result is extended for BNs with considerably restricted network structures. These results justify existing exponential time algorithms for finding control strategies for probabilistic Boolean networks (PBNs). On the other hand, this paper shows that the control problem can be solved in polynomial time if the network has a tree structure. Then, this algorithm is extended for the case where the network has a few loops and the number of time steps is small. Though this paper focuses on theoretical aspects, biological implications of the theoretical results are also discussed.

  10. Comparison of Different Artificial Neural Network (ANN) Architectures in Modeling of Chlorella sp. Flocculation.

    PubMed

    Zenooz, Alireza Moosavi; Ashtiani, Farzin Zokaee; Ranjbar, Reza; Nikbakht, Fatemeh; Bolouri, Oberon

    2017-01-03

    Biodiesel production from microalgae feedstock should be performed after growth and harvesting of the cells and the most feasible method for harvesting and dewatering of microalgae is flocculation. Flocculation modeling can be used for evaluation and prediction of its performance under different affective parameters. However, the modeling of flocculation in microalgae is not simple and has not performed yet, under all experimental conditions, mostly due to different behaviors of microalgae cells during the process under different flocculation conditions. In the current study, the modeling of microalgae flocculation is studied with different neural network architectures. Microalgae specie, Chlorella sp., was flocculated with ferric chloride under different conditions and then the experimental data modeled using artificial neural network (ANN). Neural network architectures of Multilayer Perceptron (MLP) and Radial Basis Function (RBF) architectures, failed to predict the targets successfully, though, modeling was effective with ensemble architecture of MLP networks. Comparison between the performances of the ensemble and each individual network explains the ability of the ensemble architecture in microalgae flocculation modeling.

  11. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns

    SciTech Connect

    Bornholdt, S.; Graudenz, D.

    1993-07-01

    A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.

  12. A study of satellite communication network architectures providing multipoint-to-multipoint connection

    NASA Astrophysics Data System (ADS)

    Ohata, K.

    2002-01-01

    multicasting are currently being sought. Satellite networks can efficiently handle various types of connections because each earth station can directly access all of the satellite network traffic. A multipoint to multipoint (M to N) connection is one such example. Satellite networks consume only M+N network resources, while terrestrial networks basically consume MxN network resources. In the past, there has been little demand for the M-to-N connection type; however, Internet technology is changing this situation. Take for example the case of a network for contents distribution network (CDN) service operators. CDN service operators distribute contents from content providers to caching servers deployed on major Internet service provider (ISP) backbone networks throughout the Internet. Here, a satellite network is the most suitable means of CDN, because both multicasting and M-to-N connection functions are required. However, few satellite systems can handle this type of connection properly. are well accepted as de facto systems for providing high speed Internet access, because they are advantageous in terms of both cost and user terminal size. A DVB VSAT system designed based on a broadcasting network is quite suitable for a network that has a server - client type centralized architecture, but in other cases is not suitable, such as when the network has a de-centralized architecture. In contrast, a network for M-to-N connection can be more efficiently constructed on a de-centralized architecture than on a centralized architecture. already operational. These are candidates for networks that provide efficient M-to-N connection, but their flexibility is a major issue. such as flexible connection, and by nature are suitable for de-centralized networks. In particular, FDMA meshed VSAT systems can be constructed simply, thus ensuring low cost. Also, recently developed technologies can compensate for some drawbacks of the system. The increasing TWTA power on satellites will

  13. Implementation of the FDK algorithm for cone-beam CT on the cell broadband engine architecture

    NASA Astrophysics Data System (ADS)

    Scherl, Holger; Koerner, Mario; Hofmann, Hannes; Eckert, Wieland; Kowarschik, Markus; Hornegger, Joachim

    2007-03-01

    In most of today's commercially available cone-beam CT scanners, the well known FDK method is used for solving the 3D reconstruction task. The computational complexity of this algorithm prohibits its use for many medical applications without hardware acceleration. The brand-new Cell Broadband Engine Architecture (CBEA) with its high level of parallelism is a cost-efficient processor for performing the FDK reconstruction according to the medical requirements. The programming scheme, however, is quite different to any standard personal computer hardware. In this paper, we present an innovative implementation of the most time-consuming parts of the FDK algorithm: filtering and back-projection. We also explain the required transformations to parallelize the algorithm for the CBEA. Our software framework allows to compute the filtering and back-projection in parallel, making it possible to do an on-the-fly-reconstruction. The achieved results demonstrate that a complete FDK reconstruction is computed with the CBEA in less than seven seconds for a standard clinical scenario. Given the fact that scan times are usually much higher, we conclude that reconstruction is finished right after the end of data acquisition. This enables us to present the reconstructed volume to the physician in real-time, immediately after the last projection image has been acquired by the scanning device.

  14. Algorithm and implementation of GPS/VRS network RTK

    NASA Astrophysics Data System (ADS)

    Gao, Chengfa; Yuan, Benyin; Ke, Fuyang; Pan, Shuguo

    2009-06-01

    This paper presents a virtual reference station method and its application. Details of how to generate GPS virtual phase observation are discussed in depth. The developed algorithms are successfully applied to the independent development network digital land investigation system. Experiments are carried out to investigate the system's performance whose results show that the algorithms have good availability and stability. The resulted accuracy of the VRS/RTK positioning was found to be within +/-3.3cm in the horizontal component and +/-7.9cm in the vertical component, which meets the requirements of precise digital land investigation.

  15. Exploratory Inquiry: Disparate Air Force Base Area Network Architectures

    DTIC Science & Technology

    2005-03-01

    Lieutenant Jamie Sharkey conducted a thesis on the key issues pertaining to Air Force enterprise architecture management. Discussion of...experience in fixed and tactical communications. His first duty station was McGuire AFB, New Jersey followed by assignments to Spangdahlem AB, Germany; the

  16. Fiber linked distributed data acquisition in an open network architecture

    SciTech Connect

    Pawelski, F.J.

    1995-03-01

    Flexible and easily expanded process control systems can be achieved through the use of an open and distributed architecture. This paper discusses how to achieve a truly open and distributed process control system as well as some of the goals, concerns and advantages of such a system.

  17. Parallel genetic algorithm for the design of neural networks: an application to the classification of remotely sensed data

    NASA Astrophysics Data System (ADS)

    Stramaglia, Sebastiano; Satalino, Giuseppe; Sternieri, A.; Anelli, P.; Blonda, Palma N.; Pasquariello, Guido

    1998-10-01

    We consider the problem of classification of remote sensed data from LANDSAT Thematic Mapper images. The data have been acquired in July 1986 on an area locate din South Italy. We compare the performance obtained by feed-forward neural networks designed by a parallel genetic algorithm to determine their topology with the ones obtained by means of a multi-layer perceptron trained with Back Propagation learning rule. The parallel genetic algorithm, implemented on the APE100/Quadrics platform, is based on the coding scheme recently proposed by Sternieri and Anelli and exploits a recently proposed environment for genetic algorithms on Quadrics, called AGAPE. The SASIMD architecture of Quadrics forces the chromosome representation. The coding scheme provides that the connections weights of the neural network are organized as a floating point string. The parallelization scheme adopted is the elitistic coarse grained stepping stone model, with migration occurring only towards neighboring processors. The fitness function depends on the mean square error.After fixing the total number of individuals and running the algorithm on Quadrics architectures with different number of processors, the proposed parallel genetic algorithm displayed a superlinear speedup. We report results obtained on a data set made of 1400 patterns.

  18. A Study of Artificial Neural Network Architectures for Othello Evaluation Functions

    NASA Astrophysics Data System (ADS)

    Binkley, Kevin J.; Seehart, Ken; Hagiwara, Masafumi

    In this study, we use temporal difference learning (TDL) to investigate the ability of 20 different artificial neural network (ANN) architectures to learn othello game board evaluation functions. The ANN evaluation functions are applied to create a strong othello player using only 1-ply search. In addition to comparing many of the ANN architectures seen in the literature, we introduce several new architectures that consider the game board symmetry. Both embedding the game board symmetry into the network architecture through weight sharing and the outright removal of symmetry through symmetry removal are explored. Experiments varying the number of inputs per game board square from one to three, the number of hidden nodes, and number of hidden layers are also performed. We found it advantageous to consider game board symmetry in the form of symmetry by weight sharing; and that an input encoding of three inputs per square outperformed the one input per square encoding that is commonly seen in the literature. Furthermore, architectures with only one hidden layer were strongly outperformed by architectures with multiple hidden layers. A standard weighted-square board heuristic evaluation function from the literature was used to evaluate the quality of the trained ANN othello players. One of the ANN architectures introduced in this study, an ANN implementing weight sharing and consisting of three hidden layers, using only a 1-ply search, outperformed a weighted-square test heuristic player using a 6-ply minimax search.

  19. Simulating and Synthesizing Substructures Using Neural Network and Genetic Algorithms

    NASA Technical Reports Server (NTRS)

    Liu, Youhua; Kapania, Rakesh K.; VanLandingham, Hugh F.

    1997-01-01

    The feasibility of simulating and synthesizing substructures by computational neural network models is illustrated by investigating a statically indeterminate beam, using both a 1-D and a 2-D plane stress modelling. The beam can be decomposed into two cantilevers with free-end loads. By training neural networks to simulate the cantilever responses to different loads, the original beam problem can be solved as a match-up between two subsystems under compatible interface conditions. The genetic algorithms are successfully used to solve the match-up problem. Simulated results are found in good agreement with the analytical or FEM solutions.

  20. Computer architecture for efficient algorithmic executions in real-time systems: New technology for avionics systems and advanced space vehicles

    NASA Technical Reports Server (NTRS)

    Carroll, Chester C.; Youngblood, John N.; Saha, Aindam

    1987-01-01

    Improvements and advances in the development of computer architecture now provide innovative technology for the recasting of traditional sequential solutions into high-performance, low-cost, parallel system to increase system performance. Research conducted in development of specialized computer architecture for the algorithmic execution of an avionics system, guidance and control problem in real time is described. A comprehensive treatment of both the hardware and software structures of a customized computer which performs real-time computation of guidance commands with updated estimates of target motion and time-to-go is presented. An optimal, real-time allocation algorithm was developed which maps the algorithmic tasks onto the processing elements. This allocation is based on the critical path analysis. The final stage is the design and development of the hardware structures suitable for the efficient execution of the allocated task graph. The processing element is designed for rapid execution of the allocated tasks. Fault tolerance is a key feature of the overall architecture. Parallel numerical integration techniques, tasks definitions, and allocation algorithms are discussed. The parallel implementation is analytically verified and the experimental results are presented. The design of the data-driven computer architecture, customized for the execution of the particular algorithm, is discussed.

  1. Modulation of the brain's functional network architecture in the transition from wake to sleep.

    PubMed

    Larson-Prior, Linda J; Power, Jonathan D; Vincent, Justin L; Nolan, Tracy S; Coalson, Rebecca S; Zempel, John; Snyder, Abraham Z; Schlaggar, Bradley L; Raichle, Marcus E; Petersen, Steven E

    2011-01-01

    The transition from quiet wakeful rest to sleep represents a period over which attention to the external environment fades. Neuroimaging methodologies have provided much information on the shift in neural activity patterns in sleep, but the dynamic restructuring of human brain networks in the transitional period from wake to sleep remains poorly understood. Analysis of electrophysiological measures and functional network connectivity of these early transitional states shows subtle shifts in network architecture that are consistent with reduced external attentiveness and increased internal and self-referential processing. Further, descent to sleep is accompanied by the loss of connectivity in anterior and posterior portions of the default-mode network and more locally organized global network architecture. These data clarify the complex and dynamic nature of the transitional period between wake and sleep and suggest the need for more studies investigating the dynamics of these processes.

  2. Cascaded VLSI neural network architecture for on-line learning

    NASA Technical Reports Server (NTRS)

    Thakoor, Anilkumar P. (Inventor); Duong, Tuan A. (Inventor); Daud, Taher (Inventor)

    1992-01-01

    High-speed, analog, fully-parallel, and asynchronous building blocks are cascaded for larger sizes and enhanced resolution. A hardware compatible algorithm permits hardware-in-the-loop learning despite limited weight resolution. A computation intensive feature classification application was demonstrated with this flexible hardware and new algorithm at high speed. This result indicates that these building block chips can be embedded as an application specific coprocessor for solving real world problems at extremely high data rates.

  3. Cascaded VLSI neural network architecture for on-line learning

    NASA Technical Reports Server (NTRS)

    Duong, Tuan A. (Inventor); Daud, Taher (Inventor); Thakoor, Anilkumar P. (Inventor)

    1995-01-01

    High-speed, analog, fully-parallel and asynchronous building blocks are cascaded for larger sizes and enhanced resolution. A hardware-compatible algorithm permits hardware-in-the-loop learning despite limited weight resolution. A comparison-intensive feature classification application has been demonstrated with this flexible hardware and new algorithm at high speed. This result indicates that these building block chips can be embedded as application-specific-coprocessors for solving real-world problems at extremely high data rates.

  4. Problems Related to Parallelization of CFD Algorithms on GPU, Multi-GPU and Hybrid Architectures

    NASA Astrophysics Data System (ADS)

    Biazewicz, Marek; Kurowski, Krzysztof; Ludwiczak, Bogdan; Napieraia, Krystyna

    2010-09-01

    Computational Fluid Dynamics (CFD) is one of the branches of fluid mechanics, which uses numerical methods and algorithms to solve and analyze fluid flows. CFD is used in various domains, such as oil and gas reservoir uncertainty analysis, aerodynamic body shapes optimization (e.g. planes, cars, ships, sport helmets, skis), natural phenomena analysis, numerical simulation for weather forecasting or realistic visualizations. CFD problem is very complex and needs a lot of computational power to obtain the results in a reasonable time. We have implemented a parallel application for two-dimensional CFD simulation with a free surface approximation (MAC method) using new hardware architectures, in particular multi-GPU and hybrid computing environments. For this purpose we decided to use NVIDIA graphic cards with CUDA environment due to its simplicity of programming and good computations performance. We used finite difference discretization of Navier-Stokes equations, where fluid is propagated over an Eulerian Grid. In this model, the behavior of the fluid inside the cell depends only on the properties of local, surrounding cells, therefore it is well suited for the GPU-based architecture. In this paper we demonstrate how to use efficiently the computing power of GPUs for CFD. Additionally, we present some best practices to help users analyze and improve the performance of CFD applications executed on GPU. Finally, we discuss various challenges around the multi-GPU implementation on the example of matrix multiplication.

  5. Region-Oriented Placement Algorithm for Coarse-Grained Power-Gating FPGA Architecture

    NASA Astrophysics Data System (ADS)

    Li, Ce; Dong, Yiping; Watanabe, Takahiro

    An FPGA plays an essential role in industrial products due to its fast, stable and flexible features. But the power consumption of FPGAs used in portable devices is one of critical issues. Top-down hierarchical design method is commonly used in both ASIC and FPGA design. But, in the case where plural modules are integrated in an FPGA and some of them might be in sleep-mode, current FPGA architecture cannot be fully effective. In this paper, coarse-grained power gating FPGA architecture is proposed where a whole area of an FPGA is partitioned into several regions and power supply is controlled for each region, so that modules in sleep mode can be effectively power-off. We also propose a region oriented FPGA placement algorithm fitted to this user's hierarchical design based on VPR[1]. Simulation results show that this proposed method could reduce power consumption of FPGA by 38% on average by setting unused modules or regions in sleep mode.

  6. Utilising eduroam[TM] Architecture in Building Wireless Community Networks

    ERIC Educational Resources Information Center

    Huhtanen, Karri; Vatiainen, Heikki; Keski-Kasari, Sami; Harju, Jarmo

    2008-01-01

    Purpose: eduroam[TM] has already been proved to be a scalable, secure and feasible way for universities and research institutions to connect their wireless networks into a WLAN roaming community, but the advantages of eduroam[TM] have not yet been fully discovered in the wireless community networks aimed at regular consumers. This aim of this…

  7. MODA: an efficient algorithm for network motif discovery in biological networks.

    PubMed

    Omidi, Saeed; Schreiber, Falk; Masoudi-Nejad, Ali

    2009-10-01

    In recent years, interest has been growing in the study of complex networks. Since Erdös and Rényi (1960) proposed their random graph model about 50 years ago, many researchers have investigated and shaped this field. Many indicators have been proposed to assess the global features of networks. Recently, an active research area has developed in studying local features named motifs as the building blocks of networks. Unfortunately, network motif discovery is a computationally hard problem and finding rather large motifs (larger than 8 nodes) by means of current algorithms is impractical as it demands too much computational effort. In this paper, we present a new algorithm (MODA) that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently. We have tested our algorithm and found it able to identify larger motifs with more than 8 nodes more efficiently than most of the current state-of-the-art motif discovery algorithms. While most of the algorithms rely on induced subgraphs as motifs of the networks, MODA is able to extract both induced and non-induced subgraphs simultaneously. The MODA source code is freely available at: http://LBB.ut.ac.ir/Download/LBBsoft/MODA/

  8. Adaptive Load-Balancing Algorithms Using Symmetric Broadcast Networks

    NASA Technical Reports Server (NTRS)

    Das, Sajal K.; Biswas, Rupak; Chancellor, Marisa K. (Technical Monitor)

    1997-01-01

    In a distributed-computing environment, it is important to ensure that the processor workloads are adequately balanced. Among numerous load-balancing algorithms, a unique approach due to Dam and Prasad defines a symmetric broadcast network (SBN) that provides a robust communication pattern among the processors in a topology-independent manner. In this paper, we propose and analyze three novel SBN-based load-balancing algorithms, and implement them on an SP2. A thorough experimental study with Poisson-distributed synthetic loads demonstrates that these algorithms are very effective in balancing system load while minimizing processor idle time. They also compare favorably with several other existing load-balancing techniques. Additional experiments performed with real data demonstrate that the SBN approach is effective in adaptive computational science and engineering applications where dynamic load balancing is extremely crucial.

  9. Functional clustering algorithm for the analysis of dynamic network data

    NASA Astrophysics Data System (ADS)

    Feldt, S.; Waddell, J.; Hetrick, V. L.; Berke, J. D.; Żochowski, M.

    2009-05-01

    We formulate a technique for the detection of functional clusters in discrete event data. The advantage of this algorithm is that no prior knowledge of the number of functional groups is needed, as our procedure progressively combines data traces and derives the optimal clustering cutoff in a simple and intuitive manner through the use of surrogate data sets. In order to demonstrate the power of this algorithm to detect changes in network dynamics and connectivity, we apply it to both simulated neural spike train data and real neural data obtained from the mouse hippocampus during exploration and slow-wave sleep. Using the simulated data, we show that our algorithm performs better than existing methods. In the experimental data, we observe state-dependent clustering patterns consistent with known neurophysiological processes involved in memory consolidation.

  10. Experiments on neural network architectures for fuzzy logic

    NASA Technical Reports Server (NTRS)

    Keller, James M.

    1991-01-01

    The use of fuzzy logic to model and manage uncertainty in a rule-based system places high computational demands on an inference engine. In an earlier paper, the authors introduced a trainable neural network structure for fuzzy logic. These networks can learn and extrapolate complex relationships between possibility distributions for the antecedents and consequents in the rules. Here, the power of these networks is further explored. The insensitivity of the output to noisy input distributions (which are likely if the clauses are generated from real data) is demonstrated as well as the ability of the networks to internalize multiple conjunctive clause and disjunctive clause rules. Since different rules with the same variables can be encoded in a single network, this approach to fuzzy logic inference provides a natural mechanism for rule conflict resolution.

  11. Neural-network diagnostic algorithm and smart sensor

    NASA Astrophysics Data System (ADS)

    Banish, Michele R.; Anderson, Roland B.; Ranganath, Heggere S.; Jones, Brian; Kirsch, James C.

    2000-05-01

    Digital image interpretation is the basis of medical diagnoses. Through extensive review of patient data, an algorithm was developed to identify features of diagnostic importance in radiological images. The algorithm is generally applicable. Results from cardiac, lung, and military imagery are reported. The algorithm uses a pulse coupled neural network. It is this neural network that is fabricated on a custom CMOS chip. Each neuron of the pulse coupled neural network accepts an external optical input. The optical input is accomplished by a photo-detector. The neurons communicate laterally through a voltage grid. The communication strength, light sensitivity and other global parameters are under external control. A programmable logic array is on the camera board. Data for a specific neuron is accessed by an addressing scheme typically used for a CCD array. The individual neuron speed ranges from 10 to 50 Mhertz, and is fixed by a digital clock. The current chip is configured to operate at 300 Hertz. The chip logic is a hybrid of analog and digital circuitry to minimize the neuron size, maximize the number of neurons at a fixed cost. The hybrid circuitry also minimized the noise level in the chip.

  12. MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm

    PubMed Central

    Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L.

    2014-01-01

    The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339

  13. Framework for waveband switching in multigranular optical networks: part I-multigranular cross-connect architectures [Invited

    NASA Astrophysics Data System (ADS)

    Cao, Xiaojun; Anand, Vishal; Qiao, Chunming

    2006-12-01

    Optical networks using wavelength-division multiplexing (WDM) are the foremost solution to the ever-increasing traffic in the Internet backbone. Rapid advances in WDM technology will enable each fiber to carry hundreds or even a thousand wavelengths (using dense-WDM, or DWDM, and ultra-DWDM) of traffic. This, coupled with worldwide fiber deployment, will bring about a tremendous increase in the size of the optical cross-connects, i.e., the number of ports of the wavelength switching elements. Waveband switching (WBS), wherein wavelengths are grouped into bands and switched as a single entity, can reduce the cost and control complexity of switching nodes by minimizing the port count. This paper presents a detailed study on recent advances and open research issues in WBS networks. In this study, we investigate in detail the architecture for various WBS cross-connects and compare them in terms of the number of ports and complexity and also in terms of how flexible they are in adjusting to dynamic traffic. We outline various techniques for grouping wavelengths into bands for the purpose of WBS and show how traditional wavelength routing is different from waveband routing and why techniques developed for wavelength-routed networks (WRNs) cannot be simply applied to WBS networks. We also outline how traffic grooming of subwavelength traffic can be done in WBS networks. In part II of this study [Cao , submitted to J. Opt. Netw.], we study the effect of wavelength conversion on the performance of WBS networks with reconfigurable MG-OXCs. We present an algorithm for waveband grouping in wavelength-convertible networks and evaluate its performance. We also investigate issues related to survivability in WBS networks and show how waveband and wavelength conversion can be used to recover from failures in WBS networks.

  14. Graphene-network-backboned architectures for high-performance lithium storage.

    PubMed

    Gong, Yongji; Yang, Shubin; Liu, Zheng; Ma, Lulu; Vajtai, Robert; Ajayan, Pulickel M

    2013-08-07

    An efficient hydrothermal approach is demonstrated to fabricate a series of graphene-network-backboned hybrid architectures such as MoS₂/graphene and FeOx/graphene, showing high specific surface area, porous structure, and continuous graphene networks. Such unique architectures exhibit a high reversible capacity (about 1100 mA h g⁻¹) for lithium ion batteries. High-rate capabilities of full charge to discharge in 25-45 s with a long cycle life (1500 cycles) are achieved at different rates.

  15. A Novel Approach to Noise-Filtering Based on a Gain-Scheduling Neural Network Architecture

    NASA Technical Reports Server (NTRS)

    Troudet, T.; Merrill, W.

    1994-01-01

    A gain-scheduling neural network architecture is proposed to enhance the noise-filtering efficiency of feedforward neural networks, in terms of both nominal performance and robustness. The synergistic benefits of the proposed architecture are demonstrated and discussed in the context of the noise-filtering of signals that are typically encountered in aerospace control systems. The synthesis of such a gain-scheduled neurofiltering provides the robustness of linear filtering, while preserving the nominal performance advantage of conventional nonlinear neurofiltering. Quantitative performance and robustness evaluations are provided for the signal processing of pitch rate responses to typical pilot command inputs for a modern fighter aircraft model.

  16. Private algorithms for the protected in social network search

    PubMed Central

    Kearns, Michael; Roth, Aaron; Wu, Zhiwei Steven; Yaroslavtsev, Grigory

    2016-01-01

    Motivated by tensions between data privacy for individual citizens and societal priorities such as counterterrorism and the containment of infectious disease, we introduce a computational model that distinguishes between parties for whom privacy is explicitly protected, and those for whom it is not (the targeted subpopulation). The goal is the development of algorithms that can effectively identify and take action upon members of the targeted subpopulation in a way that minimally compromises the privacy of the protected, while simultaneously limiting the expense of distinguishing members of the two groups via costly mechanisms such as surveillance, background checks, or medical testing. Within this framework, we provide provably privacy-preserving algorithms for targeted search in social networks. These algorithms are natural variants of common graph search methods, and ensure privacy for the protected by the careful injection of noise in the prioritization of potential targets. We validate the utility of our algorithms with extensive computational experiments on two large-scale social network datasets. PMID:26755606

  17. Analysis of normal human retinal vascular network architecture using multifractal geometry

    PubMed Central

    Ţălu, Ştefan; Stach, Sebastian; Călugăru, Dan Mihai; Lupaşcu, Carmen Alina; Nicoară, Simona Delia

    2017-01-01

    AIM To apply the multifractal analysis method as a quantitative approach to a comprehensive description of the microvascular network architecture of the normal human retina. METHODS Fifty volunteers were enrolled in this study in the Ophthalmological Clinic of Cluj-Napoca, Romania, between January 2012 and January 2014. A set of 100 segmented and skeletonised human retinal images, corresponding to normal states of the retina were studied. An automatic unsupervised method for retinal vessel segmentation was applied before multifractal analysis. The multifractal analysis of digital retinal images was made with computer algorithms, applying the standard box-counting method. Statistical analyses were performed using the GraphPad InStat software. RESULTS The architecture of normal human retinal microvascular network was able to be described using the multifractal geometry. The average of generalized dimensions (Dq) for q=0, 1, 2, the width of the multifractal spectrum (Δα=αmax − αmin) and the spectrum arms' heights difference (|Δf|) of the normal images were expressed as mean±standard deviation (SD): for segmented versions, D0=1.7014±0.0057; D1=1.6507±0.0058; D2=1.5772±0.0059; Δα=0.92441±0.0085; |Δf|= 0.1453±0.0051; for skeletonised versions, D0=1.6303±0.0051; D1=1.6012±0.0059; D2=1.5531±0.0058; Δα=0.65032±0.0162; |Δf|= 0.0238±0.0161. The average of generalized dimensions (Dq) for q=0, 1, 2, the width of the multifractal spectrum (Δα) and the spectrum arms' heights difference (|Δf|) of the segmented versions was slightly greater than the skeletonised versions. CONCLUSION The multifractal analysis of fundus photographs may be used as a quantitative parameter for the evaluation of the complex three-dimensional structure of the retinal microvasculature as a potential marker for early detection of topological changes associated with retinal diseases. PMID:28393036

  18. An Architectural Concept for Intrusion Tolerance in Air Traffic Networks

    NASA Technical Reports Server (NTRS)

    Maddalon, Jeffrey M.; Miner, Paul S.

    2003-01-01

    The goal of an intrusion tolerant network is to continue to provide predictable and reliable communication in the presence of a limited num ber of compromised network components. The behavior of a compromised network component ranges from a node that no longer responds to a nod e that is under the control of a malicious entity that is actively tr ying to cause other nodes to fail. Most current data communication ne tworks do not include support for tolerating unconstrained misbehavio r of components in the network. However, the fault tolerance communit y has developed protocols that provide both predictable and reliable communication in the presence of the worst possible behavior of a limited number of nodes in the system. One may view a malicious entity in a communication network as a node that has failed and is behaving in an arbitrary manner. NASA/Langley Research Center has developed one such fault-tolerant computing platform called SPIDER (Scalable Proces sor-Independent Design for Electromagnetic Resilience). The protocols and interconnection mechanisms of SPIDER may be adapted to large-sca le, distributed communication networks such as would be required for future Air Traffic Management systems. The predictability and reliabi lity guarantees provided by the SPIDER protocols have been formally v erified. This analysis can be readily adapted to similar network stru ctures.

  19. The comparison of network congestion avoidance algorithms in data exchange networks.

    NASA Astrophysics Data System (ADS)

    Grzyb, S.; Orłowski, P.

    2017-01-01

    Effective congestion control strategies allow in maintaining low delay and high throughput in data exchange networks. These requirements seem to be the most desired by network environment participants. Wide range of algorithms are proposed in the literature to approximate to these ideal parameters. All of these approaches are focused on alleviating the results of sudden, unexpected network state changes. This paper discuss a comparison of four control strategies, focused on congestion avoidance. All of them are in charge of queue length control in active network nodes. For research purpose, non-stationary, discrete, dynamical model of communication channel has been used. Research results are presented in table and chart form.

  20. Prolonging the lifetime of wireless sensor networks interconnected to fixed network using hierarchical energy tree based routing algorithm.

    PubMed

    Kalpana, M; Dhanalakshmi, R; Parthiban, P

    2014-01-01

    This research work proposes a mathematical model for the lifetime of wireless sensor networks (WSN). It also proposes an energy efficient routing algorithm for WSN called hierarchical energy tree based routing algorithm (HETRA) based on hierarchical energy tree constructed using the available energy in each node. The energy efficiency is further augmented by reducing the packet drops using exponential congestion control algorithm (TCP/EXP). The algorithms are evaluated in WSNs interconnected to fixed network with seven distribution patterns, simulated in ns2 and compared with the existing algorithms based on the parameters such as number of data packets, throughput, network lifetime, and data packets average network lifetime product. Evaluation and simulation results show that the combination of HETRA and TCP/EXP maximizes longer network lifetime in all the patterns. The lifetime of the network with HETRA algorithm has increased approximately 3.2 times that of the network implemented with AODV.

  1. Architectural Design for the Global Legal Information Network

    NASA Technical Reports Server (NTRS)

    Kalpakis, Konstantinos

    1999-01-01

    In this report, we provide a summary of our activities regarding the goals, requirements analysis, design, and prototype implementation for the Global Legal Information Network, a joint effort between the Law Library of Congress and NASA.

  2. Data mining algorithms for wireless sensor network's data

    NASA Astrophysics Data System (ADS)

    Muntean, Maria; Vălean, Honoriu; Tulbure, Adrian; Ileană, Ioan; Kadar, Manuella

    2010-11-01

    Classification of sensory data is a major research problem in wireless sensor networks and it can be widely used in reducing the data transmission in wireless sensor networks effectively and also in process monitoring. In order to examine the huge size of data set in stream model generated by sensor network, it will be analyzed different sensor's output signal, topology of sensors network, number of sensor parameters and number of acquisition data. In our wind energy monitoring, sensor node monitors six attributes: speed, direction, temperature, pressure, humidity, and battery voltage. Every attribute value is set as four measures: average, instantaneous, minimum, and maximum. This paper presents several data mining techniques applied on the wireless sensor network's data considered: Naïve Bayes, k-nearest neighbor, decision trees, IF-THEN rules, and neural networks. Before classification, the data was clustered in order to be labeled. A similarity based algorithm, k-means, was selected in the clustering process for its simplicity and efficiency. A conclusion that decision trees are a suitable method to classify the large amount of data considered is made finally according to the mining result and its reasonable explanation.

  3. A gene network simulator to assess reverse engineering algorithms.

    PubMed

    Di Camillo, Barbara; Toffolo, Gianna; Cobelli, Claudio

    2009-03-01

    In the context of reverse engineering of biological networks, simulators are helpful to test and compare the accuracy of different reverse-engineering approaches in a variety of experimental conditions. A novel gene-network simulator is presented that resembles some of the main features of transcriptional regulatory networks related to topology, interaction among regulators of transcription, and expression dynamics. The simulator generates network topology according to the current knowledge of biological network organization, including scale-free distribution of the connectivity and clustering coefficient independent of the number of nodes in the network. It uses fuzzy logic to represent interactions among the regulators of each gene, integrated with differential equations to generate continuous data, comparable to real data for variety and dynamic complexity. Finally, the simulator accounts for saturation in the response to regulation and transcription activation thresholds and shows robustness to perturbations. It therefore provides a reliable and versatile test bed for reverse engineering algorithms applied to microarray data. Since the simulator describes regulatory interactions and expression dynamics as two distinct, although interconnected aspects of regulation, it can also be used to test reverse engineering approaches that use both microarray and protein-protein interaction data in the process of learning. A first software release is available at http://www.dei.unipd.it/~dicamill/software/netsim as an R programming language package.

  4. An Integrated Architecture to Support Hastily Formed Network (HFN)

    DTIC Science & Technology

    2007-12-01

    Figure 8. EU 1000I, EU 2000I and EU 3000IS...............................................................52 Figure 9. Inmarsat BGAN Satellite Coverage...Hughes 9201 Broadband Satellite IP Terminal with Wireless LAN Access Point, which interfaces over the Inmarsat BGAN satellite network, has many...advantages that make it an attractive solution. (1) Global Coverage BGAN delivers seamless network coverage across most of the world’s land mass

  5. SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks.

    PubMed

    Adabor, Emmanuel S; Acquaah-Mensah, George K; Oduro, Francis T

    2015-02-01

    Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks.

  6. Performance evaluation of a routing algorithm based on Hopfield Neural Network for network-on-chip

    NASA Astrophysics Data System (ADS)

    Esmaelpoor, Jamal; Ghafouri, Abdollah

    2015-12-01

    Network on chip (NoC) has emerged as a solution to overcome the system on chip growing complexity and design challenges. A proper routing algorithm is a key issue of an NoC design. An appropriate routing method balances load across the network channels and keeps path length as short as possible. This survey investigates the performance of a routing algorithm based on Hopfield Neural Network. It is a dynamic programming to provide optimal path and network monitoring in real time. The aim of this article is to analyse the possibility of using a neural network as a router. The algorithm takes into account the path with the lowest delay (cost) form source to destination. In other words, the path a message takes from source to destination depends on network traffic situation at the time and it is the fastest one. The simulation results show that the proposed approach improves average delay, throughput and network congestion efficiently. At the same time, the increase in power consumption is almost negligible.

  7. SANDS: A Service-Oriented Architecture for Clinical Decision Support in a National Health Information Network

    PubMed Central

    Wright, Adam; Sittig, Dean F.

    2008-01-01

    In this paper we describe and evaluate a new distributed architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support), which leverages current health information exchange efforts and is based on the principles of a service-oriented architecture. The architecture allows disparate clinical information systems and clinical decision support systems to be seamlessly integrated over a network according to a set of interfaces and protocols described in this paper. The architecture described is fully defined and developed, and six use cases have been developed and tested using a prototype electronic health record which links to one of the existing prototype National Health Information Networks (NHIN): drug interaction checking, syndromic surveillance, diagnostic decision support, inappropriate prescribing in older adults, information at the point of care and a simple personal health record. Some of these use cases utilize existing decision support systems, which are either commercially or freely available at present, and developed outside of the SANDS project, while other use cases are based on decision support systems developed specifically for the project. Open source code for many of these components is available, and an open source reference parser is also available for comparison and testing of other clinical information systems and clinical decision support systems that wish to implement the SANDS architecture. PMID:18434256

  8. Experimental demonstration of high spectral-efficiency transmission with a novel non-interferometric vector modulator and custom DSP algorithms for coherent PON architectures

    NASA Astrophysics Data System (ADS)

    Lazarou, I.; Spatharakis, C.; Katopodis, V.; Dris, S.; Bakopoulos, P.; Schrenk, B.; Avramopoulos, H.

    2013-12-01

    We demonstrate a flexible multi-format vector modulator for coherent passive optical network (PON) architectures based on a non-interferometric cascade of a Semiconductor Optical Amplifier (SOA) and an Electro-Absorption Modulator (EAM) that exhibits increased simplicity and high energy-efficiency at low cost. 12- and 16- Quadrature Amplitude Modulation (QAM) constellations are generated, achieving modulation efficiencies of 3.6 and 4 bits/symbol respectively. The presented results demonstrate successful transmission over 25 km of fiber below the Forward Error Correction (FEC) limit, with sufficient compatible loss-budgets, making the proposed modulator concept an attractive candidate for urban network deployments, where high user density demands spectrally efficient formats. In addition, we describe the operation principle of the proposed modulator in detail and finally, the complete set of the digital signal processing (DSP) functionalities and algorithms that follow the standard coherent detection scheme is given, emphasizing on novel methods for QAM signal demodulation with uncommon constellation diagrams.

  9. A New High-Resolution Direction Finding Architecture Using Photonics and Neural Network Signal Processing for Miniature Air Vehicle Applications

    DTIC Science & Technology

    2015-09-01

    RESOLUTION DIRECTION FINDING ARCHITECTURE USING PHOTONICS AND NEURAL NETWORK SIGNAL PROCESSING FOR MINIATURE AIR VEHICLE APPLICATIONS by Robert...RESOLUTION DIRECTION FINDING ARCHITECTURE USING PHOTONICS AND NEURAL NETWORK SIGNAL PROCESSING FOR MINIATURE AIR VEHICLE APPLICATIONS 5. FUNDING...unlimited 12b. DISTRIBUTION CODE 13. ABSTRACT (maximum 200 words) This paper investigates the design of an interferometric direction finding receiver

  10. The P-Mesh: A Commodity-based Scalable Network Architecture for Clusters

    NASA Technical Reports Server (NTRS)

    Nitzberg, Bill; Kuszmaul, Chris; Stockdale, Ian; Becker, Jeff; Jiang, John; Wong, Parkson; Tweten, David (Technical Monitor)

    1998-01-01

    We designed a new network architecture, the P-Mesh which combines the scalability and fault resilience of a torus with the performance of a switch. We compare the scalability, performance, and cost of the hub, switch, torus, tree, and P-Mesh architectures. The latter three are capable of scaling to thousands of nodes, however, the torus has severe performance limitations with that many processors. The tree and P-Mesh have similar latency, bandwidth, and bisection bandwidth, but the P-Mesh outperforms the switch architecture (a lower bound for tree performance) on 16-node NAB Parallel Benchmark tests by up to 23%, and costs 40% less. Further, the P-Mesh has better fault resilience characteristics. The P-Mesh architecture trades increased management overhead for lower cost, and is a good bridging technology while the price of tree uplinks is expensive.

  11. Architecture of the human regulatory network derived from ENCODE data.

    PubMed

    Gerstein, Mark B; Kundaje, Anshul; Hariharan, Manoj; Landt, Stephen G; Yan, Koon-Kiu; Cheng, Chao; Mu, Xinmeng Jasmine; Khurana, Ekta; Rozowsky, Joel; Alexander, Roger; Min, Renqiang; Alves, Pedro; Abyzov, Alexej; Addleman, Nick; Bhardwaj, Nitin; Boyle, Alan P; Cayting, Philip; Charos, Alexandra; Chen, David Z; Cheng, Yong; Clarke, Declan; Eastman, Catharine; Euskirchen, Ghia; Frietze, Seth; Fu, Yao; Gertz, Jason; Grubert, Fabian; Harmanci, Arif; Jain, Preti; Kasowski, Maya; Lacroute, Phil; Leng, Jing; Lian, Jin; Monahan, Hannah; O'Geen, Henriette; Ouyang, Zhengqing; Partridge, E Christopher; Patacsil, Dorrelyn; Pauli, Florencia; Raha, Debasish; Ramirez, Lucia; Reddy, Timothy E; Reed, Brian; Shi, Minyi; Slifer, Teri; Wang, Jing; Wu, Linfeng; Yang, Xinqiong; Yip, Kevin Y; Zilberman-Schapira, Gili; Batzoglou, Serafim; Sidow, Arend; Farnham, Peggy J; Myers, Richard M; Weissman, Sherman M; Snyder, Michael

    2012-09-06

    Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.

  12. Advancing Reversible Shape Memory by Tuning Network Architecture

    NASA Astrophysics Data System (ADS)

    Li, Qiaoxi; Zhou, Jing; Vatankhah Varnosfaderani, Mohammad; Nykypanchuk, Dmytro; Gang, Oleg; Sheiko, Sergei; University of north carolina at chapel hill Collaboration; Brookhaven National Lab-CFN Collaboration

    Recently, reversible shape memory (RSM) has been realized in conventional semi-crystalline elastomers without applying any external force and synthetic programming. The mechanism is ascribed to counteraction between thermodynamically driven relaxation of a strained polymer network and kinetically preferred self-seeding recrystallization of constrained network strands. In order to maximize RSM's performance in terms of (i) range of reversible strain, (ii) rate of strain recovery, and (iii) relaxation time of reversibility, we have designed a systematic series of networks with different topologies and crosslinking densities, including purposely introduced dangling chains and irregular meshes. Within a broad range of crosslink density ca. 50-1000 mol/m3, we have demonstrated that the RSM's properties improve significantly with increasing crosslink density, regardless of network topology. Actually, one of the most irregular networks with densest crosslinking allowed achieving up to 80% of the programmed strain being fully reversible, fast recovery rate up to 0.05 K-1, and less than 15% decrease of reversibility after hours of annealing at partial melt state. With this understanding and optimization of RSM, we pursue an idea of shape control through self-assembly of shape-memory particles. For this purpose, 3D printing has been employed to prepare large assemblies of particles possessing specific shapes and morphologies.

  13. Habitat loss alters the architecture of plant--pollinator interaction networks.

    PubMed

    Spiesman, Brian J; Inouye, Brian D

    2013-12-01

    Habitat loss can have a negative effect on the number, abundance, and composition of species in plant-pollinator communities. Although we have a general understanding of the negative consequences of habitat loss for biodiversity, much less is known about the resulting effects on the pattern of interactions in mutualistic networks. Ecological networks formed by mutualistic interactions often exhibit a highly nested architecture with low modularity, especially in comparison with antagonistic networks. These patterns of interaction are thought to confer stability on mutualistic communities. With the growing threat of environmental change, it is important to expand our understanding of the factors that affect biodiversity and the stability of the communities that provide critical ecosystem functions and services. We studied the effects of habitat loss on plant--pollinator network architecture and found that regional habitat loss contributes directly to species loss and indirectly to the reorganization of interspecific interactions in a local community. Networks became more highly connected and more modular with habitat loss. Species richness and abundance were the primary drivers of variation in network architecture, though species compositi n affected modularity. Theory suggests that an increase in modularity with habitat loss will threaten community stability, which may contribute to an extinction debt in communities already affected by habitat loss.

  14. Weaknesses and drawbacks of a password authentication scheme using neural networks for multiserver architecture.

    PubMed

    Ku, Wei-Chi

    2005-07-01

    In 2001, Li et al. proposed a password authentication scheme for the multiserver architecture by using a pattern classification system based on neural networks. Herein, we demonstrate that Li et al's scheme is vulnerable to an offline password guessing attack and a privileged insider's attack, and is not reparable. Additionally, we show that Li et al.'s scheme has several drawbacks in practice.

  15. Dynamic behavior of shortest path routing algorithms for communication networks

    NASA Astrophysics Data System (ADS)

    Bertsekas, D. P.

    1980-06-01

    Several proposed routing algorithms for store and forward communication networks, including one currently in operation in the ARPANET, route messages along shortest paths computed by using some set of link lengths. When these lengths depend on current traffic conditions as they must in an adaptive algorithm, dynamic behavior questions such as stability convergence, and speed of convergence are of interest. This paper is the first attempt to analyze systematically these issues. It is shown that minimum queuing delay path algorithms tend to exhibit violent oscillatory behavior in the absence of a damping mechanism. The oscillations can be damped by means of several types of schemes, two of which are analyzed in this paper. In the first scheme a constant bias is added to the queuing delay thereby providing a preference towards paths with a small number of links. In the second scheme the effects of several past routings are averaged as, for example, when the link lengths are computed and communicated asynchronously throughout the network.

  16. Architectures and economics for pervasive broadband satellite networks

    NASA Technical Reports Server (NTRS)

    Staelin, D. H.; Harvey, R. L.

    1979-01-01

    The size of a satellite network necessary to provide pervasive high-data-rate business communications is estimated, and one possible configuration is described which could interconnect most organizations in the United States. Within an order of magnitude, such a network might reasonably have a capacity equivalent to 10,000 simultaneous 3-Mbps channels, and rely primarily upon a cluster of approximately 3-5 satellites in a single orbital slot. Nominal prices for 3-6 Mbps video conference services might then be approximately $2000 monthly lease charge plus perhaps 70 cents per minute one way.

  17. WDS Knowledge Network Architecture in Support of International Science

    NASA Astrophysics Data System (ADS)

    Mokrane, M.; Minster, J. B. H.; Hugo, W.

    2014-12-01

    ICSU (International Council for Science) created the World Data System (WDS) as an interdisciplinary body at its General Assembly in Maputo in 2008, and since then the membership of the WDS has grown to include 86 members, of whom 56 are institutions or data centers focused on providing quality-assured data and services to the scientific community, and 10 more are entire networks of such data facilities and services. In addition to its objective of providing universal and equitable access to scientific data and services, WDS is also active in promoting stewardship, standards and conventions, and improved access to products derived from data and services. Whereas WDS is in process of aggregating and harmonizing the metadata collections of its membership, it is clear that additional benefits can be obtained by supplementing such traditional metadata sources with information about members, authors, and the coverages of the data, as well as metrics such as citation indices, quality indicators, and usability. Moreover, the relationships between the actors and systems that populate this metadata landscape can be seen as a knowledge network that describes a subset of global scientific endeavor. Such a knowledge network is useful in many ways, supporting both machine-based and human requests for contextual information related to a specific data set, institution, author, topic, or other entities in the network. Specific use cases that can be realized include decision and policy support for funding agencies, identification of collaborators, ranking of data sources, availability of data for specific coverages, and many more. The paper defines the scope of and conceptual background to such a knowledge network, discusses some initial work done by WDS to establish the network, and proposes an implementation model for rapid operationalization. In this model, established interests such as DataCite, ORCID, and CrossRef have well-defined roles, and the standards, services, and

  18. Reconfigurable middleware architectures for large scale sensor networks

    SciTech Connect

    Brennan, Sean M.

    2010-03-01

    Wireless sensor networks, in an e ffort to be energy efficient, typically lack the high-level abstractions of advanced programming languages. Though strong, the dichotomy between these two paradigms can be overcome. The SENSIX software framework, described in this dissertation, uniquely integrates constraint-dominated wireless sensor networks with the flexibility of object-oriented programming models, without violating the principles of either. Though these two computing paradigms are contradictory in many ways, SENSIX bridges them to yield a dynamic middleware abstraction unifying low-level resource-aware task recon figuration and high-level object recomposition.

  19. Phase transition of the microvascular network architecture in human pathologies.

    PubMed

    Bianciardi, Giorgio; Traversi, Claudio; Cattaneo, Ruggero; De Felice, Claudia; Monaco, Annalisa; Tosi, Gianmarco; Parrini, Stefano; Latini, Giuseppe

    2012-01-01

    We have investigated the microvascular pattern in acquired or genetic diseases in humans. The lower gingival and vestibular oral mucosa, as well as the optic nerve head, was chosen to characterize the vascular pattern complexity due to the simple accessibility and visibility Local fractal dimensions, fractal dimension of the minimum path and Lempel-Ziv complexity have been used as operational numerical tools to characterize the microvascular networks. In the normal healthy subjects microvascular networks show nonlinear values corresponding to the complexity of a diffusion limited aggregation (DLA) model, while in several acquired or genetic diseases they are approaching the ones of an invasion percolation model.

  20. A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications

    DOE PAGES

    James, Conrad D.; Aimone, James B.; Miner, Nadine E.; ...

    2017-01-04

    In this study, biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here in this research, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classesmore » such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. Additionally, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.« less

  1. A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications

    SciTech Connect

    James, Conrad D.; Aimone, James B.; Miner, Nadine E.; Vineyard, Craig M.; Rothganger, Fredrick H.; Carlson, Kristofor D.; Mulder, Samuel A.; Draelos, Timothy J.; Faust, Aleksandra; Marinella, Matthew J.; Naegle, John H.; Plimpton, Steven J.

    2017-01-01

    Biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here in this research, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. Additionally, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.

  2. Calibration of neural networks using genetic algorithms, with application to optimal path planning

    NASA Technical Reports Server (NTRS)

    Smith, Terence R.; Pitney, Gilbert A.; Greenwood, Daniel

    1987-01-01

    Genetic algorithms (GA) are used to search the synaptic weight space of artificial neural systems (ANS) for weight vectors that optimize some network performance function. GAs do not suffer from some of the architectural constraints involved with other techniques and it is straightforward to incorporate terms into the performance function concerning the metastructure of the ANS. Hence GAs offer a remarkably general approach to calibrating ANS. GAs are applied to the problem of calibrating an ANS that finds optimal paths over a given surface. This problem involves training an ANS on a relatively small set of paths and then examining whether the calibrated ANS is able to find good paths between arbitrary start and end points on the surface.

  3. A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks

    PubMed Central

    Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

    2016-01-01

    The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks. PMID:27754380

  4. A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks.

    PubMed

    Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

    2016-10-13

    The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

  5. Insights into Brain Architectures from the Homological Scaffolds of Functional Connectivity Networks.

    PubMed

    Lord, Louis-David; Expert, Paul; Fernandes, Henrique M; Petri, Giovanni; Van Hartevelt, Tim J; Vaccarino, Francesco; Deco, Gustavo; Turkheimer, Federico; Kringelbach, Morten L

    2016-01-01

    In recent years, the application of network analysis to neuroimaging data has provided useful insights about the brain's functional and structural organization in both health and disease. This has proven a significant paradigm shift from the study of individual brain regions in isolation. Graph-based models of the brain consist of vertices, which represent distinct brain areas, and edges which encode the presence (or absence) of a structural or functional relationship between each pair of vertices. By definition, any graph metric will be defined upon this dyadic representation of the brain activity. It is however unclear to what extent these dyadic relationships can capture the brain's complex functional architecture and the encoding of information in distributed networks. Moreover, because network representations of global brain activity are derived from measures that have a continuous response (i.e., interregional BOLD signals), it is methodologically complex to characterize the architecture of functional networks using traditional graph-based approaches. In the present study, we investigate the relationship between standard network metrics computed from dyadic interactions in a functional network, and a metric defined on the persistence homological scaffold of the network, which is a summary of the persistent homology structure of resting-state fMRI data. The persistence homological scaffold is a summary network that differs in important ways from the standard network representations of functional neuroimaging data: (i) it is constructed using the information from all edge weights comprised in the original network without applying an ad hoc threshold and (ii) as a summary of persistent homology, it considers the contributions of simplicial structures to the network organization rather than dyadic edge-vertices interactions. We investigated the information domain captured by the persistence homological scaffold by computing the strength of each node in the

  6. Dynamic Adaptive Neural Network Arrays: A Neuromorphic Architecture

    SciTech Connect

    Disney, Adam; Reynolds, John

    2015-01-01

    Dynamic Adaptive Neural Network Array (DANNA) is a neuromorphic hardware implementation. It differs from most other neuromorphic projects in that it allows for programmability of structure, and it is trained or designed using evolutionary optimization. This paper describes the DANNA structure, how DANNA is trained using evolutionary optimization, and an application of DANNA to a very simple classification task.

  7. Shaping Networked Theatre: Experience Architectures, Behaviours and Creative Pedagogies

    ERIC Educational Resources Information Center

    Sutton, Paul

    2012-01-01

    Since 2006 the UK based applied theatre company C&T has been using its experience and expertise in mixing drama, learning and digital media to create a new online utility for shaping collaborative educational drama experiences. C&T describes this practice as "Networked Theatre". This article describes both the motivations for…

  8. Context Aware Routing Management Architecture for Airborne Networks

    DTIC Science & Technology

    2012-03-22

    21 MBps Megabytes per second . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 RF Radio Frequency...collection of mobile nodes that generate a network automatically by creating several short-lived radio frequency links based on node proximity and mutual...can be lost due to to blocking , obstacles or other types of interference. Two nodes can establish communication directly in a MANET if the recipient is

  9. Knowledge Network Architecture in Support of International Science

    NASA Astrophysics Data System (ADS)

    Hugo, Wim

    2015-04-01

    ICSU (The International Council for Science) created the World Data System (WDS) as an interdisciplinary body at its General Assembly in Maputo in 2008, and since then the membership of the WDS has grown to include 86 members, of whom 56 are institutions or data centres focused on providing quality-assured data and services to the scientific community. In addition to its objective of providing universal and equitable access to such data and services, WDS is also active in promoting stewardship, standards and conventions, and improved access to products derived from data and services. Whereas WDS is in process of aggregating and harmonizing the meta-data collections of its membership, it is clear that additional benefits can be obtained by supplementing such traditional meta-data sources with information about members, authors, and the coverages of the data, as well as metrics such as citation indices, quality indicators, and usability. Moreover, the relationships between the actors and systems that populate this meta-data landscape can be seen as a knowledge network that describes a sub-set of global scientific endeavor. Such a knowledge network is useful in many ways, supporting both machine-based and human requests for contextual information related to a specific data set, institution, author, topic, or other entities in the network. Specific use cases that can be realised include decision and policy support for funding agencies, identification of collaborators, ranking of data sources, availability of data for specific coverages, and many more. The paper defines the scope of and conceptual background to such a knowledge network, discusses some initial work done by WDS to establish the network, and proposes an implementation model for rapid operationalisation. In this model, established interests such as DataCITE, ORCID, and CrossRef have well-defined roles, and the standards, services, and registries required to build a community-maintained, scalable knowledge

  10. Novel Spectrum Sensing Algorithms for OFDM Cognitive Radio Networks

    PubMed Central

    Shi, Zhenguo; Wu, Zhilu; Yin, Zhendong; Cheng, Qingqing

    2015-01-01

    Spectrum sensing technology plays an increasingly important role in cognitive radio networks. Consequently, several spectrum sensing algorithms have been proposed in the literature. In this paper, we present a new spectrum sensing algorithm “Differential Characteristics-Based OFDM (DC-OFDM)” for detecting OFDM signal on account of differential characteristics. We put the primary value on channel gain θ around zero to detect the presence of primary user. Furthermore, utilizing the same method of differential operation, we improve two traditional OFDM sensing algorithms (cyclic prefix and pilot tones detecting algorithms), and propose a “Differential Characteristics-Based Cyclic Prefix (DC-CP)” detector and a “Differential Characteristics-Based Pilot Tones (DC-PT)” detector, respectively. DC-CP detector is based on auto-correlation vector to sense the spectrum, while the DC-PT detector takes the frequency-domain cross-correlation of PT as the test statistic to detect the primary user. Moreover, the distributions of the test statistics of the three proposed methods have been derived. Simulation results illustrate that all of the three proposed methods can achieve good performance under low signal to noise ratio (SNR) with the presence of timing delay. Specifically, the DC-OFDM detector gets the best performance among the presented detectors. Moreover, both of the DC-CP and DC-PT detector achieve significant improvements compared with their corresponding original detectors. PMID:26083226

  11. Novel Spectrum Sensing Algorithms for OFDM Cognitive Radio Networks.

    PubMed

    Shi, Zhenguo; Wu, Zhilu; Yin, Zhendong; Cheng, Qingqing

    2015-06-15

    Spectrum sensing technology plays an increasingly important role in cognitive radio networks. Consequently, several spectrum sensing algorithms have been proposed in the literature. In this paper, we present a new spectrum sensing algorithm "Differential Characteristics-Based OFDM (DC-OFDM)" for detecting OFDM signal on account of differential characteristics. We put the primary value on channel gain θ around zero to detect the presence of primary user. Furthermore, utilizing the same method of differential operation, we improve two traditional OFDM sensing algorithms (cyclic prefix and pilot tones detecting algorithms), and propose a "Differential Characteristics-Based Cyclic Prefix (DC-CP)" detector and a "Differential Characteristics-Based Pilot Tones (DC-PT)" detector, respectively. DC-CP detector is based on auto-correlation vector to sense the spectrum, while the DC-PT detector takes the frequency-domain cross-correlation of PT as the test statistic to detect the primary user. Moreover, the distributions of the test statistics of the three proposed methods have been derived. Simulation results illustrate that all of the three proposed methods can achieve good performance under low signal to noise ratio (SNR) with the presence of timing delay. Specifically, the DC-OFDM detector gets the best performance among the presented detectors. Moreover, both of the DC-CP and DC-PT detector achieve significant improvements compared with their corresponding original detectors.

  12. Column generation algorithms for exact modularity maximization in networks

    NASA Astrophysics Data System (ADS)

    Aloise, Daniel; Cafieri, Sonia; Caporossi, Gilles; Hansen, Pierre; Perron, Sylvain; Liberti, Leo

    2010-10-01

    Finding modules, or clusters, in networks currently attracts much attention in several domains. The most studied criterion for doing so, due to Newman and Girvan [Phys. Rev. E 69, 026113 (2004)]10.1103/PhysRevE.69.026113, is modularity maximization. Many heuristics have been proposed for maximizing modularity and yield rapidly near optimal solution or sometimes optimal ones but without a guarantee of optimality. There are few exact algorithms, prominent among which is a paper by Xu [Eur. Phys. J. B 60, 231 (2007)]10.1140/epjb/e2007-00331-0. Modularity maximization can also be expressed as a clique partitioning problem and the row generation algorithm of Grötschel and Wakabayashi [Math. Program. 45, 59 (1989)]10.1007/BF01589097 applied. We propose to extend both of these algorithms using the powerful column generation methods for linear and non linear integer programming. Performance of the four resulting algorithms is compared on problems from the literature. Instances with up to 512 entities are solved exactly. Moreover, the computing time of previously solved problems are reduced substantially.

  13. RH+: A Hybrid Localization Algorithm for Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Basaran, Can; Baydere, Sebnem; Kucuk, Gurhan

    Today, localization of nodes in Wireless Sensor Networks (WSNs) is a challenging problem. Especially, it is almost impossible to guarantee that one algorithm giving optimal results for one topology will give optimal results for any other random topology. In this study, we propose a centralized, range- and anchor-based, hybrid algorithm called RH+ that aims to combine the powerful features of two orthogonal techniques: Classical Multi-Dimensional Scaling (CMDS) and Particle Spring Optimization (PSO). As a result, we find that our hybrid approach gives a fast-converging solution which is resilient to range-errors and very robust to topology changes. Across all topologies we studied, the average estimation error is less than 0.5m. when the average node density is 10 and only 2.5% of the nodes are beacons.

  14. Neural network implementations of data association algorithms for sensor fusion

    NASA Technical Reports Server (NTRS)

    Brown, Donald E.; Pittard, Clarence L.; Martin, Worthy N.

    1989-01-01

    The paper is concerned with locating a time varying set of entities in a fixed field when the entities are sensed at discrete time instances. At a given time instant a collection of bivariate Gaussian sensor reports is produced, and these reports estimate the location of a subset of the entities present in the field. A database of reports is maintained, which ideally should contain one report for each entity sensed. Whenever a collection of sensor reports is received, the database must be updated to reflect the new information. This updating requires association processing between the database reports and the new sensor reports to determine which pairs of sensor and database reports correspond to the same entity. Algorithms for performing this association processing are presented. Neural network implementation of the algorithms, along with simulation results comparing the approaches are provided.

  15. Spectrum handoff algorithm with imperfect spectrum sensing incognitive radio networks

    NASA Astrophysics Data System (ADS)

    Ma, Bin; Bao, Xiao-min; Xie, Xian-zhong

    2016-10-01

    To guarantee the quality of service of licensed users, the unlicensed users must vacate the occupied channel by spectrum handoff if licensed users appear in the licensed spectrum. However, spectrum sensing with false alarm and missed detection may lead to more inaccuracy problems when unlicensed users perform spectrum handoff. To improve the problems mentioned before, we utilize PRP M/G/1 queuing network model to propose a spectrum handoff algorithm with error data retransmission mechanism and analyze its performance by transmission delay and extended data delivery time. In addition, we discuss the relationship between false alarm probability, extended data delivery time and user traffic load and verify the effectiveness of the proposed algorithm.

  16. Methods of information theory and algorithmic complexity for network biology.

    PubMed

    Zenil, Hector; Kiani, Narsis A; Tegnér, Jesper

    2016-03-01

    We survey and introduce concepts and tools located at the intersection of information theory and network biology. We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. We show examples such as the emergence of giant components in Erdös-Rényi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data. We provide exact theoretical calculations, numerical approximations and error estimations of entropy, algorithmic probability and Kolmogorov complexity for different types of graphs, characterizing their variant and invariant properties. We introduce formal definitions of complexity for both labeled and unlabeled graphs and prove that the Kolmogorov complexity of a labeled graph is a good approximation of its unlabeled Kolmogorov complexity and thus a robust definition of graph complexity.

  17. APPLICATION OF NEURAL NETWORK ALGORITHMS FOR BPM LINEARIZATION

    SciTech Connect

    Musson, John C.; Seaton, Chad; Spata, Mike F.; Yan, Jianxun

    2012-11-01

    Stripline BPM sensors contain inherent non-linearities, as a result of field distortions from the pickup elements. Many methods have been devised to facilitate corrections, often employing polynomial fitting. The cost of computation makes real-time correction difficult, particulalry when integer math is utilized. The application of neural-network technology, particularly the multi-layer perceptron algorithm, is proposed as an efficient alternative for electrode linearization. A process of supervised learning is initially used to determine the weighting coefficients, which are subsequently applied to the incoming electrode data. A non-linear layer, known as an activation layer, is responsible for the removal of saturation effects. Implementation of a perceptron in an FPGA-based software-defined radio (SDR) is presented, along with performance comparisons. In addition, efficient calculation of the sigmoidal activation function via the CORDIC algorithm is presented.

  18. Hubs of Anticorrelation in High-Resolution Resting-State Functional Connectivity Network Architecture.

    PubMed

    Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Cabanban, Romeo; Crosson, Bruce A

    2015-06-01

    A major focus of brain research recently has been to map the resting-state functional connectivity (rsFC) network architecture of the normal brain and pathology through functional magnetic resonance imaging. However, the phenomenon of anticorrelations in resting-state signals between different brain regions has not been adequately examined. The preponderance of studies on resting-state fMRI (rsFMRI) have either ignored anticorrelations in rsFC networks or adopted methods in data analysis, which have rendered anticorrelations in rsFC networks uninterpretable. The few studies that have examined anticorrelations in rsFC networks using conventional methods have found anticorrelations to be weak in strength and not very reproducible across subjects. Anticorrelations in rsFC network architecture could reflect mechanisms that subserve a number of important brain processes. In this preliminary study, we examined the properties of anticorrelated rsFC networks by systematically focusing on negative cross-correlation coefficients (CCs) among rsFMRI voxel time series across the brain with graph theory-based network analysis. A number of methods were implemented to enhance the neuronal specificity of resting-state functional connections that yield negative CCs, although at the cost of decreased sensitivity. Hubs of anticorrelation were seen in a number of cortical and subcortical brain regions. Examination of the anticorrelation maps of these hubs indicated that negative CCs in rsFC network architecture highlight a number of regulatory interactions between brain networks and regions, including reciprocal modulations, suppression, inhibition, and neurofeedback.

  19. Determination of multifractal dimensions of complex networks by means of the sandbox algorithm

    NASA Astrophysics Data System (ADS)

    Liu, Jin-Long; Yu, Zu-Guo; Anh, Vo

    2015-02-01

    Complex networks have attracted much attention in diverse areas of science and technology. Multifractal analysis (MFA) is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. In this paper, we employ the sandbox (SB) algorithm proposed by Tél et al. (Physica A 159, 155-166 (1989)), for MFA of complex networks. First, we compare the SB algorithm with two existing algorithms of MFA for complex networks: the compact-box-burning algorithm proposed by Furuya and Yakubo (Phys. Rev. E 84, 036118 (2011)), and the improved box-counting algorithm proposed by Li et al. (J. Stat. Mech.: Theor. Exp. 2014, P02020 (2014)) by calculating the mass exponents τ(q) of some deterministic model networks. We make a detailed comparison between the numerical and theoretical results of these model networks. The comparison results show that the SB algorithm is the most effective and feasible algorithm to calculate the mass exponents τ(q) and to explore the multifractal behavior of complex networks. Then, we apply the SB algorithm to study the multifractal property of some classic model networks, such as scale-free networks, small-world networks, and random networks. Our results show that multifractality exists in scale-free networks, that of small-world networks is not obvious, and it almost does not exist in random networks.

  20. Implementation of pattern recognition algorithm based on RBF neural network

    NASA Astrophysics Data System (ADS)

    Bouchoux, Sophie; Brost, Vincent; Yang, Fan; Grapin, Jean Claude; Paindavoine, Michel

    2002-12-01

    In this paper, we present implementations of a pattern recognition algorithm which uses a RBF (Radial Basis Function) neural network. Our aim is to elaborate a quite efficient system which realizes real time faces tracking and identity verification in natural video sequences. Hardware implementations have been realized on an embedded system developed by our laboratory. This system is based on a DSP (Digital Signal Processor) TMS320C6x. The optimization of implementations allow us to obtain a processing speed of 4.8 images (240x320 pixels) per second with a correct rate of 95% of faces tracking and identity verification.

  1. Intelligent Middle-Ware Architecture for Mobile Networks

    NASA Astrophysics Data System (ADS)

    Rayana, Rayene Ben; Bonnin, Jean-Marie

    Recent advances in electronic and automotive industries as well as in wireless telecommunication technologies have drawn a new picture where each vehicle became “fully networked”. Multiple stake-holders (network operators, drivers, car manufacturers, service providers, etc.) will participate in this emerging market, which could grow following various models. To free the market from technical constraints, it is important to return to the basics of the Internet, i.e., providing embarked devices with a fully operational Internet connectivity (IPv6).

  2. A proposed architecture for a satellite-based mobile communications network - The lowest three layers

    NASA Technical Reports Server (NTRS)

    Yan, T. Y.; Naderi, F. M.

    1986-01-01

    Architecture for a commercial mobile satellite network is proposed. The mobile satellite system (MSS) is composed of a network management center, mobile terminals, base stations, and gateways; the functions of each component are described. The satellite is a 'bent pipe' that performs frequency translations, and it has multiple UHF beams. The development of the MSS design based on the seven-layer open system interconnection model is examined. Consideration is given to the functions of the physical, data link, and network layers and the integrated adaptive mobile access protocol.

  3. Synchronization in heterogeneous FitzHugh-Nagumo networks with hierarchical architecture.

    PubMed

    Plotnikov, S A; Lehnert, J; Fradkov, A L; Schöll, E

    2016-07-01

    We study synchronization in heterogeneous FitzHugh-Nagumo networks. It is well known that heterogeneities in the nodes hinder synchronization when becoming too large. Here we develop a controller to counteract the impact of these heterogeneities. We first analyze the stability of the equilibrium point in a ring network of heterogeneous nodes. We then derive a sufficient condition for synchronization in the absence of control. Based on these results we derive the controller providing synchronization for parameter values where synchronization without control is absent. We demonstrate our results in networks with different topologies. Particular attention is given to hierarchical (fractal) topologies, which are relevant for the architecture of the brain.

  4. Synchronization in heterogeneous FitzHugh-Nagumo networks with hierarchical architecture

    NASA Astrophysics Data System (ADS)

    Plotnikov, S. A.; Lehnert, J.; Fradkov, A. L.; Schöll, E.

    2016-07-01

    We study synchronization in heterogeneous FitzHugh-Nagumo networks. It is well known that heterogeneities in the nodes hinder synchronization when becoming too large. Here we develop a controller to counteract the impact of these heterogeneities. We first analyze the stability of the equilibrium point in a ring network of heterogeneous nodes. We then derive a sufficient condition for synchronization in the absence of control. Based on these results we derive the controller providing synchronization for parameter values where synchronization without control is absent. We demonstrate our results in networks with different topologies. Particular attention is given to hierarchical (fractal) topologies, which are relevant for the architecture of the brain.

  5. Hierarchical network architectures of carbon fiber paper supported cobalt oxide nanonet for high-capacity pseudocapacitors.

    PubMed

    Yang, Lei; Cheng, Shuang; Ding, Yong; Zhu, Xingbao; Wang, Zhong Lin; Liu, Meilin

    2012-01-11

    We present a high-capacity pseudocapacitor based on a hierarchical network architecture consisting of Co(3)O(4) nanowire network (nanonet) coated on a carbon fiber paper. With this tailored architecture, the electrode shows ideal capacitive behavior (rectangular shape of cyclic voltammograms) and large specific capacitance (1124 F/g) at high charge/discharge rate (25.34 A/g), still retaining ~94% of the capacitance at a much lower rate of 0.25 A/g. The much-improved capacity, rate capability, and cycling stability may be attributed to the unique hierarchical network structures, which improves electron/ion transport, enhances the kinetics of redox reactions, and facilitates facile stress relaxation during cycling.

  6. Planning assistance for the NASA 30/20 GHz program. Network control architecture study.

    NASA Technical Reports Server (NTRS)

    Inukai, T.; Bonnelycke, B.; Strickland, S.

    1982-01-01

    Network Control Architecture for a 30/20 GHz flight experiment system operating in the Time Division Multiple Access (TDMA) was studied. Architecture development, identification of processing functions, and performance requirements for the Master Control Station (MCS), diversity trunking stations, and Customer Premises Service (CPS) stations are covered. Preliminary hardware and software processing requirements as well as budgetary cost estimates for the network control system are given. For the trunking system control, areas covered include on board SS-TDMA switch organization, frame structure, acquisition and synchronization, channel assignment, fade detection and adaptive power control, on board oscillator control, and terrestrial network timing. For the CPS control, they include on board processing and adaptive forward error correction control.

  7. CombiMotif: A new algorithm for network motifs discovery in protein-protein interaction networks

    NASA Astrophysics Data System (ADS)

    Luo, Jiawei; Li, Guanghui; Song, Dan; Liang, Cheng

    2014-12-01

    Discovering motifs in protein-protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMotif) that incorporates combinatorial techniques to count non-induced occurrences of subgraph topologies in the form of trees. The efficiency of our algorithm is demonstrated by comparing the obtained results with the current state-of-the art subgraph counting algorithms. We also show major differences between unicellular and multicellular organisms. The datasets and source code of CombiMotif are freely available upon request.

  8. Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks.

    PubMed

    Alshinina, Remah; Elleithy, Khaled

    2017-03-08

    Wireless Sensor Networks (WSNs) have become essential components for a variety of environmental, surveillance, military, traffic control, and healthcare applications. These applications face critical challenges such as communication, security, power consumption, data aggregation, heterogeneities of sensor hardware, and Quality of Service (QoS) issues. Service-Oriented Architecture (SOA) is a software architecture that can be integrated with WSN applications to address those challenges. The SOA middleware bridges the gap between the high-level requirements of different applications and the hardware constraints of WSNs. This survey explores state-of-the-art approaches based on SOA and Service-Oriented Middleware (SOM) architecture that provide solutions for WSN challenges. The categories of this paper are based on approaches of SOA with and without middleware for WSNs. Additionally, features of SOA and middleware architectures for WSNs are compared to achieve more robust and efficient network performance. Design issues of SOA middleware for WSNs and its characteristics are also highlighted. The paper concludes with future research directions in SOM architecture to meet all requirements of emerging application of WSNs.

  9. Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks

    PubMed Central

    Alshinina, Remah; Elleithy, Khaled

    2017-01-01

    Wireless Sensor Networks (WSNs) have become essential components for a variety of environmental, surveillance, military, traffic control, and healthcare applications. These applications face critical challenges such as communication, security, power consumption, data aggregation, heterogeneities of sensor hardware, and Quality of Service (QoS) issues. Service-Oriented Architecture (SOA) is a software architecture that can be integrated with WSN applications to address those challenges. The SOA middleware bridges the gap between the high-level requirements of different applications and the hardware constraints of WSNs. This survey explores state-of-the-art approaches based on SOA and Service-Oriented Middleware (SOM) architecture that provide solutions for WSN challenges. The categories of this paper are based on approaches of SOA with and without middleware for WSNs. Additionally, features of SOA and middleware architectures for WSNs are compared to achieve more robust and efficient network performance. Design issues of SOA middleware for WSNs and its characteristics are also highlighted. The paper concludes with future research directions in SOM architecture to meet all requirements of emerging application of WSNs. PMID:28282896

  10. Algorithmic Perspectives of Network Transitive Reduction Problems and their Applications to Synthesis and Analysis of Biological Networks

    PubMed Central

    Aditya, Satabdi; DasGupta, Bhaskar; Karpinski, Marek

    2013-01-01

    In this survey paper, we will present a number of core algorithmic questions concerning several transitive reduction problems on network that have applications in network synthesis and analysis involving cellular processes. Our starting point will be the so-called minimum equivalent digraph problem, a classic computational problem in combinatorial algorithms. We will subsequently consider a few non-trivial extensions or generalizations of this problem motivated by applications in systems biology. We will then discuss the applications of these algorithmic methodologies in the context of three major biological research questions: synthesizing and simplifying signal transduction networks, analyzing disease networks, and measuring redundancy of biological networks. PMID:24833332

  11. Distributed learning automata-based algorithm for community detection in complex networks

    NASA Astrophysics Data System (ADS)

    Khomami, Mohammad Mehdi Daliri; Rezvanian, Alireza; Meybodi, Mohammad Reza

    2016-03-01

    Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

  12. Fusion techniques of fuzzy systems and neural networks, and fuzzy systems and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Takagi, Hideyuki

    1993-12-01

    This paper overviews four combinations of fuzzy logic, neural networks and genetic algorithms: (1) neural networks to auto-design fuzzy systems, (2) employing fuzzy rule structure to construct structured neural networks, (3) genetic algorithms to auto-design fuzzy systems, and (4) a fuzzy knowledge-based system to control genetic parameter dynamically.

  13. A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

    NASA Astrophysics Data System (ADS)

    Tahmasebi, Pejman; Hezarkhani, Ardeshir

    2012-05-01

    The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called "Coactive Neuro-Fuzzy Inference System" (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) - as a well-known technique to solve the complex optimization problems - is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS-GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS-GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems.

  14. A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation

    PubMed Central

    Tahmasebi, Pejman; Hezarkhani, Ardeshir

    2012-01-01

    The grade estimation is a quite important and money/time-consuming stage in a mine project, which is considered as a challenge for the geologists and mining engineers due to the structural complexities in mineral ore deposits. To overcome this problem, several artificial intelligence techniques such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) have recently been employed with various architectures and properties. However, due to the constraints of both methods, they yield the desired results only under the specific circumstances. As an example, one major problem in FL is the difficulty of constructing the membership functions (MFs).Other problems such as architecture and local minima could also be located in ANN designing. Therefore, a new methodology is presented in this paper for grade estimation. This method which is based on ANN and FL is called “Coactive Neuro-Fuzzy Inference System” (CANFIS) which combines two approaches, ANN and FL. The combination of these two artificial intelligence approaches is achieved via the verbal and numerical power of intelligent systems. To improve the performance of this system, a Genetic Algorithm (GA) – as a well-known technique to solve the complex optimization problems – is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input. A comparison of these techniques (ANN, Adaptive Neuro-Fuzzy Inference System or ANFIS) with this new method (CANFIS–GA) is also carried out through a case study in Sungun copper deposit, located in East-Azerbaijan, Iran. The results show that CANFIS–GA could be a faster and more accurate alternative to the existing time-consuming methodologies for ore grade estimation and that is, therefore, suggested to be applied for grade estimation in similar problems. PMID:25540468

  15. Research on mixed network architecture collaborative application model

    NASA Astrophysics Data System (ADS)

    Jing, Changfeng; Zhao, Xi'an; Liang, Song

    2009-10-01

    When facing complex requirements of city development, ever-growing spatial data, rapid development of geographical business and increasing business complexity, collaboration between multiple users and departments is needed urgently, however conventional GIS software (such as Client/Server model or Browser/Server model) are not support this well. Collaborative application is one of the good resolutions. Collaborative application has four main problems to resolve: consistency and co-edit conflict, real-time responsiveness, unconstrained operation, spatial data recoverability. In paper, application model called AMCM is put forward based on agent and multi-level cache. AMCM can be used in mixed network structure and supports distributed collaborative. Agent is an autonomous, interactive, initiative and reactive computing entity in a distributed environment. Agent has been used in many fields such as compute science and automation. Agent brings new methods for cooperation and the access for spatial data. Multi-level cache is a part of full data. It reduces the network load and improves the access and handle of spatial data, especially, in editing the spatial data. With agent technology, we make full use of its characteristics of intelligent for managing the cache and cooperative editing that brings a new method for distributed cooperation and improves the efficiency.

  16. Architecture of the parallel hierarchical network for fast image recognition

    NASA Astrophysics Data System (ADS)

    Timchenko, Leonid; Wójcik, Waldemar; Kokriatskaia, Natalia; Kutaev, Yuriy; Ivasyuk, Igor; Kotyra, Andrzej; Smailova, Saule

    2016-09-01

    Multistage integration of visual information in the brain allows humans to respond quickly to most significant stimuli while maintaining their ability to recognize small details in the image. Implementation of this principle in technical systems can lead to more efficient processing procedures. The multistage approach to image processing includes main types of cortical multistage convergence. The input images are mapped into a flexible hierarchy that reflects complexity of image data. Procedures of the temporal image decomposition and hierarchy formation are described in mathematical expressions. The multistage system highlights spatial regularities, which are passed through a number of transformational levels to generate a coded representation of the image that encapsulates a structure on different hierarchical levels in the image. At each processing stage a single output result is computed to allow a quick response of the system. The result is presented as an activity pattern, which can be compared with previously computed patterns on the basis of the closest match. With regard to the forecasting method, its idea lies in the following. In the results synchronization block, network-processed data arrive to the database where a sample of most correlated data is drawn using service parameters of the parallel-hierarchical network.

  17. Coupling root architecture and pore network modeling - an attempt towards better understanding root-soil interactions

    NASA Astrophysics Data System (ADS)

    Leitner, Daniel; Bodner, Gernot; Raoof, Amir

    2013-04-01

    Understanding root-soil interactions is of high importance for environmental and agricultural management. Root uptake is an essential component in water and solute transport modeling. The amount of groundwater recharge and solute leaching significantly depends on the demand based plant extraction via its root system. Plant uptake however not only responds to the potential demand, but in most situations is limited by supply form the soil. The ability of the plant to access water and solutes in the soil is governed mainly by root distribution. Particularly under conditions of heterogeneous distribution of water and solutes in the soil, it is essential to capture the interaction between soil and roots. Root architecture models allow studying plant uptake from soil by describing growth and branching of root axes in the soil. Currently root architecture models are able to respond dynamically to water and nutrient distribution in the soil by directed growth (tropism), modified branching and enhanced exudation. The porous soil medium as rooting environment in these models is generally described by classical macroscopic water retention and sorption models, average over the pore scale. In our opinion this simplified description of the root growth medium implies several shortcomings for better understanding root-soil interactions: (i) It is well known that roots grow preferentially in preexisting pores, particularly in more rigid/dry soil. Thus the pore network contributes to the architectural form of the root system; (ii) roots themselves can influence the pore network by creating preferential flow paths (biopores) which are an essential element of structural porosity with strong impact on transport processes; (iii) plant uptake depend on both the spatial location of water/solutes in the pore network as well as the spatial distribution of roots. We therefore consider that for advancing our understanding in root-soil interactions, we need not only to extend our root models

  18. PNNI routing support for ad hoc mobile networking: A flat architecture

    SciTech Connect

    Martinez, L.; Sholander, P.; Tolendino, L.

    1997-12-01

    This contribution extends the Outside Nodal Hierarchy List (ONHL) procedures described in ATM Form Contribution 97-0766. These extensions allow multiple mobile networks to form either an ad hoc network or an extension of a fixed PNNI infrastructure. This contribution covers the simplest case where the top-most Logical Group Nodes (LGNs), in those mobile networks, all reside at the same level in a PNNI hierarchy. Future contributions will cover the general case where those top-most LGNs reside at different hierarchy levels. This contribution considers a flat ad hoc network architecture--in the sense that each mobile network always participates in the PNNI hierarchy at the preconfigured level of its top-most LGN.

  19. Node Non-Uniform Deployment Based on Clustering Algorithm for Underwater Sensor Networks

    PubMed Central

    Jiang, Peng; Liu, Jun; Wu, Feng

    2015-01-01

    A node non-uniform deployment based on clustering algorithm for underwater sensor networks (UWSNs) is proposed in this study. This algorithm is proposed because optimizing network connectivity rate and network lifetime is difficult for the existing node non-uniform deployment algorithms under the premise of improving the network coverage rate for UWSNs. A high network connectivity rate is achieved by determining the heterogeneous communication ranges of nodes during node clustering. Moreover, the concept of aggregate contribution degree is defined, and the nodes with lower aggregate contribution degrees are used to substitute the dying nodes to decrease the total movement distance of nodes and prolong the network lifetime. Simulation results show that the proposed algorithm can achieve a better network coverage rate and network connectivity rate, as well as decrease the total movement distance of nodes and prolong the network lifetime. PMID:26633408

  20. Hierarchical artificial bee colony algorithm for RFID network planning optimization.

    PubMed

    Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

    2014-01-01

    This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

  1. Survivable architectures for time and wavelength division multiplexed passive optical networks

    NASA Astrophysics Data System (ADS)

    Wong, Elaine

    2014-08-01

    The increased network reach and customer base of next-generation time and wavelength division multiplexed PON (TWDM-PONs) have necessitated rapid fault detection and subsequent restoration of services to its users. However, direct application of existing solutions for conventional PONs to TWDM-PONs is unsuitable as these schemes rely on the loss of signal (LOS) of upstream transmissions to trigger protection switching. As TWDM-PONs are required to potentially use sleep/doze mode optical network units (ONU), the loss of upstream transmission from a sleeping or dozing ONU could erroneously trigger protection switching. Further, TWDM-PONs require its monitoring modules for fiber/device fault detection to be more sensitive than those typically deployed in conventional PONs. To address the above issues, three survivable architectures that are compliant with TWDM-PON specifications are presented in this work. These architectures combine rapid detection and protection switching against multipoint failure, and most importantly do not rely on upstream transmissions for LOS activation. Survivability analyses as well as evaluations of the additional costs incurred to achieve survivability are performed and compared to the unprotected TWDM-PON. Network parameters that impact the maximum achievable network reach, maximum split ratio, connection availability, fault impact, and the incremental reliability costs for each proposed survivable architecture are highlighted.

  2. Using Hybrid Algorithm to Improve Intrusion Detection in Multi Layer Feed Forward Neural Networks

    ERIC Educational Resources Information Center

    Ray, Loye Lynn

    2014-01-01

    The need for detecting malicious behavior on a computer networks continued to be important to maintaining a safe and secure environment. The purpose of this study was to determine the relationship of multilayer feed forward neural network architecture to the ability of detecting abnormal behavior in networks. This involved building, training, and…

  3. A Sensitive Secondary Users Selection Algorithm for Cognitive Radio Ad Hoc Networks

    PubMed Central

    Li, Aohan; Han, Guangjie; Wan, Liangtian; Shu, Lei

    2016-01-01

    Secondary Users (SUs) are allowed to use the temporarily unused licensed spectrum without disturbing Primary Users (PUs) in Cognitive Radio Ad Hoc Networks (CRAHNs). Existing architectures for CRAHNs impose energy-consuming Cognitive Radios (CRs) on SUs. However, the advanced CRs will increase energy cost for their cognitive functionalities, which is undesirable for the battery powered devices. A new architecture referred to as spectral Requirement-based CRAHN (RCRAHN) is proposed to enhance energy efficiency for CRAHNs in this paper. In RCRAHNs, only parts of SUs are equipped with CRs. SUs equipped with CRs are referred to as Cognitive Radio Users (CRUs). To further enhance energy efficiency of CRAHNs, we aim to select minimum CRUs to sense available spectrum. A non-linear programming problem is mathematically formulated under the constraints of energy efficiency and real-time. Considering the NP-hardness of the problem, a framework of a heuristic algorithm referred to as Sensitive Secondary Users Selection (SSUS) was designed to compute the near-optimal solutions. The simulation results demonstrate that SSUS not only improves the energy efficiency, but also achieves satisfied performances in end-to-end delay and communication reliability. PMID:27023562

  4. Performance analysis of distributed symmetric sparse matrix vector multiplication algorithm for multi-core architectures

    DOE PAGES

    Oryspayev, Dossay; Aktulga, Hasan Metin; Sosonkina, Masha; ...

    2015-07-14

    In this article, sparse matrix vector multiply (SpMVM) is an important kernel that frequently arises in high performance computing applications. Due to its low arithmetic intensity, several approaches have been proposed in literature to improve its scalability and efficiency in large scale computations. In this paper, our target systems are high end multi-core architectures and we use messaging passing interface + open multiprocessing hybrid programming model for parallelism. We analyze the performance of recently proposed implementation of the distributed symmetric SpMVM, originally developed for large sparse symmetric matrices arising in ab initio nuclear structure calculations. We also study important featuresmore » of this implementation and compare with previously reported implementations that do not exploit underlying symmetry. Our SpMVM implementations leverage the hybrid paradigm to efficiently overlap expensive communications with computations. Our main comparison criterion is the "CPU core hours" metric, which is the main measure of resource usage on supercomputers. We analyze the effects of topology-aware mapping heuristic using simplified network load model. Furthermore, we have tested the different SpMVM implementations on two large clusters with 3D Torus and Dragonfly topology. Our results show that the distributed SpMVM implementation that exploits matrix symmetry and hides communication yields the best value for the "CPU core hours" metric and significantly reduces data movement overheads.« less

  5. Performance analysis of distributed symmetric sparse matrix vector multiplication algorithm for multi-core architectures

    SciTech Connect

    Oryspayev, Dossay; Aktulga, Hasan Metin; Sosonkina, Masha; Maris, Pieter; Vary, James P.

    2015-07-14

    In this article, sparse matrix vector multiply (SpMVM) is an important kernel that frequently arises in high performance computing applications. Due to its low arithmetic intensity, several approaches have been proposed in literature to improve its scalability and efficiency in large scale computations. In this paper, our target systems are high end multi-core architectures and we use messaging passing interface + open multiprocessing hybrid programming model for parallelism. We analyze the performance of recently proposed implementation of the distributed symmetric SpMVM, originally developed for large sparse symmetric matrices arising in ab initio nuclear structure calculations. We also study important features of this implementation and compare with previously reported implementations that do not exploit underlying symmetry. Our SpMVM implementations leverage the hybrid paradigm to efficiently overlap expensive communications with computations. Our main comparison criterion is the "CPU core hours" metric, which is the main measure of resource usage on supercomputers. We analyze the effects of topology-aware mapping heuristic using simplified network load model. Furthermore, we have tested the different SpMVM implementations on two large clusters with 3D Torus and Dragonfly topology. Our results show that the distributed SpMVM implementation that exploits matrix symmetry and hides communication yields the best value for the "CPU core hours" metric and significantly reduces data movement overheads.

  6. C-element: a new clustering algorithm to find high quality functional modules in PPI networks.

    PubMed

    Ghasemi, Mahdieh; Rahgozar, Maseud; Bidkhori, Gholamreza; Masoudi-Nejad, Ali

    2013-01-01

    Graph clustering algorithms are widely used in the analysis of biological networks. Extracting functional modules in protein-protein interaction (PPI) networks is one such use. Most clustering algorithms whose focuses are on finding functional modules try either to find a clique like sub networks or to grow clusters starting from vertices with high degrees as seeds. These algorithms do not make any difference between a biological network and any other networks. In the current research, we present a new procedure to find functional modules in PPI networks. Our main idea is to model a biological concept and to use this concept for finding good functional modules in PPI networks. In order to evaluate the quality of the obtained clusters, we compared the results of our algorithm with those of some other widely used clustering algorithms on three high throughput PPI networks from Sacchromyces Cerevisiae, Homo sapiens and Caenorhabditis elegans as well as on some tissue specific networks. Gene Ontology (GO) analyses were used to compare the results of different algorithms. Each algorithm's result was then compared with GO-term derived functional modules. We also analyzed the effect of using tissue specific networks on the quality of the obtained clusters. The experimental results indicate that the new algorithm outperforms most of the others, and this improvement is more significant when tissue specific networks are used.

  7. Teledesic Global Wireless Broadband Network: Space Infrastructure Architecture, Design Features and Technologies

    NASA Technical Reports Server (NTRS)

    Stuart, James R.

    1995-01-01

    The Teledesic satellites are a new class of small satellites which demonstrate the important commercial benefits of using technologies developed for other purposes by U.S. National Laboratories. The Teledesic satellite architecture, subsystem design features, and new technologies are described. The new Teledesic satellite manufacturing, integration, and test approaches which use modern high volume production techniques and result in surprisingly low space segment costs are discussed. The constellation control and management features and attendant software architecture features are addressed. After briefly discussing the economic and technological impact on the USA commercial space industries of the space communications revolution and such large constellation projects, the paper concludes with observations on the trend toward future system architectures using networked groups of much smaller satellites.

  8. Novel L1 neural network adaptive control architecture with guaranteed transient performance.

    PubMed

    Cao, Chengyu; Hovakimyan, Naira

    2007-07-01

    In this paper, we present a novel neural network (NN) adaptive control architecture with guaranteed transient performance. With this new architecture, both input and output signals of an uncertain nonlinear system follow a desired linear system during the transient phase, in addition to stable tracking. This new architecture uses a low-pass filter in the feedback loop, which consequently enables to enforce the desired transient performance by increasing the adaptation gain. For the guaranteed transient performance of both input and output signals of the uncertain nonlinear system, the L1 gain of a cascaded system, comprised of the low-pass filter and the closed-loop desired reference model, is required to be less than the inverse of the Lipschitz constant of the unknown nonlinearities in the system. The tools from this paper can be used to develop a theoretically justified verification and validation framework for NN adaptive controllers. Simulation results illustrate the theoretical findings.

  9. A TDMA Broadcast Satellite/Ground Architecture for the Aeronautical Telecommunications Network

    NASA Technical Reports Server (NTRS)

    Shamma, Mohammed A.; Raghavan, Rajesh S.

    2003-01-01

    An initial evaluation of a TDMA satellite broadcast architecture with an integrated ground network is proposed in this study as one option for the Aeronautical Telecommunications Network (ATN). The architecture proposed consists of a ground based network that is dedicated to the reception and transmissions of Automatic Dependent Surveillance Broadcast (ADS-B) messages from Mode-S or UAT type systems, along with tracks from primary and secondary surveillance radars. Additionally, the ground network could contain VHF Digital Link Mode 2, 3 or 4 transceivers for the reception and transmissions of Controller-Pilot Data Link Communications (CPDLC) messages and for voice. The second part of the ATN network consists of a broadcast satellite based system that is mainly dedicated for the transmission of surveillance data as well as En-route Flight Information Service Broadcast (FIS-B) to all aircraft. The system proposed integrates those two network to provide a nation wide comprehensive service utilizing near term or existing technologies and hence keeping the economic factor in prospective. The next few sections include a background introduction, the ground subnetwork, the satellite subnetwork, modeling and simulations, and conclusion and recommendations.

  10. Nogo-A regulates vascular network architecture in the postnatal brain.

    PubMed

    Wälchli, Thomas; Ulmann-Schuler, Alexandra; Hintermüller, Christoph; Meyer, Eric; Stampanoni, Marco; Carmeliet, Peter; Emmert, Maximilian Y; Bozinov, Oliver; Regli, Luca; Schwab, Martin E; Vogel, Johannes; Hoerstrup, Simon P

    2017-02-01

    Recently, we discovered a new role for the well-known axonal growth inhibitory molecule Nogo-A as a negative regulator of angiogenesis in the developing central nervous system. However, how Nogo-A affected the three-dimensional (3D) central nervous system (CNS) vascular network architecture remained unknown. Here, using vascular corrosion casting, hierarchical, synchrotron radiation μCT-based network imaging and computer-aided network analysis, we found that genetic ablation of Nogo-A significantly increased the three-dimensional vascular volume fraction in the postnatal day 10 (P10) mouse brain. More detailed analysis of the cerebral cortex revealed that this effect was mainly due to an increased number of capillaries and capillary branchpoints. Interestingly, other vascular parameters such as vessel diameter, -length, -tortuosity, and -volume were comparable between both genotypes for non-capillary vessels and capillaries. Taken together, our three-dimensional data showing more vessel segments and branchpoints at unchanged vessel morphology suggest that stimulated angiogenesis upon Nogo-A gene deletion results in the insertion of complete capillary micro-networks and not just single vessels into existing vascular networks. These findings significantly enhance our understanding of how angiogenesis, vascular remodeling, and three-dimensional vessel network architecture are regulated during central nervous system development. Nogo-A may therefore be a potential novel target for angiogenesis-dependent central nervous system pathologies such as brain tumors or stroke.

  11. A feed-forward Hopfield neural network algorithm (FHNNA) with a colour satellite image for water quality mapping

    NASA Astrophysics Data System (ADS)

    Asal Kzar, Ahmed; Mat Jafri, M. Z.; Hwee San, Lim; Al-Zuky, Ali A.; Mutter, Kussay N.; Hassan Al-Saleh, Anwar

    2016-06-01

    There are many techniques that have been given for water quality problem, but the remote sensing techniques have proven their success, especially when the artificial neural networks are used as mathematical models with these techniques. Hopfield neural network is one type of artificial neural networks which is common, fast, simple, and efficient, but it when it deals with images that have more than two colours such as remote sensing images. This work has attempted to solve this problem via modifying the network that deals with colour remote sensing images for water quality mapping. A Feed-forward Hopfield Neural Network Algorithm (FHNNA) was modified and used with a satellite colour image from type of Thailand earth observation system (THEOS) for TSS mapping in the Penang strait, Malaysia, through the classification of TSS concentrations. The new algorithm is based essentially on three modifications: using HNN as feed-forward network, considering the weights of bitplanes, and non-self-architecture or zero diagonal of weight matrix, in addition, it depends on a validation data. The achieved map was colour-coded for visual interpretation. The efficiency of the new algorithm has found out by the higher correlation coefficient (R=0.979) and the lower root mean square error (RMSE=4.301) between the validation data that were divided into two groups. One used for the algorithm and the other used for validating the results. The comparison was with the minimum distance classifier. Therefore, TSS mapping of polluted water in Penang strait, Malaysia, can be performed using FHNNA with remote sensing technique (THEOS). It is a new and useful application of HNN, so it is a new model with remote sensing techniques for water quality mapping which is considered important environmental problem.

  12. Heterogeneous sensor networks: a bio-inspired overlay architecture

    NASA Astrophysics Data System (ADS)

    Burman, Jerry; Hespanha, Joao; Madhow, Upamanyu; Klein, Daniel; Isaacs, Jason; Venkateswaran, Sriram; Pham, Tien

    2010-04-01

    Teledyne Scientific Company, the University of California at Santa Barbara (UCSB) and the Army Research Lab are developing technologies for automated data exfiltration from heterogeneous sensor networks through the Institute for Collaborative Biotechnologies (ICB). Unmanned air vehicles (UAV) provide an effective means to autonomously collect data from unattended ground sensors (UGSs) that cannot communicate with each other. UAVs are used to reduce the system reaction time by generating autonomous data-driven collection routes. Bio-inspired techniques for search provide a novel strategy to detect, capture and fuse data across heterogeneous sensors. A fast and accurate method has been developed for routing UAVs and localizing an event by fusing data from a sparse number of UGSs; it leverages a bio-inspired technique based on chemotaxis or the motion of bacteria seeking nutrients in their environment. The system was implemented and successfully tested using a high level simulation environment using a flight simulator to emulate a UAV. A field test was also conducted in November 2009 at Camp Roberts, CA using a UAV provided by AeroMech Engineering. The field test results showed that the system can detect and locate the source of an acoustic event with an accuracy of about 3 meters average circular error.

  13. Issues impacting genetic network reverse engineering algorithm validation using small networks.

    PubMed

    Vinh, Nguyen Xuan; Chetty, Madhu; Coppel, Ross; Wangikar, Pramod P

    2012-12-01

    Genetic network reverse engineering has been an area of intensive research within the systems biology community during the last decade. With many techniques currently available, the task of validating them and choosing the best one for a certain problem is a complex issue. Current practice has been to validate an approach on in-silico synthetic data sets, and, wherever possible, on real data sets with known ground-truth. In this study, we highlight a major issue that the validation of reverse engineering algorithms on small benchmark networks very often results in networks which are not statistically better than a randomly picked network. Another important issue highlighted is that with short time series, a small variation in the pre-processing procedure might yield large differences in the inferred networks. To demonstrate these issues, we have selected as our case study the IRMA in-vivo synthetic yeast network recently published in Cell. Using Fisher's exact test, we show that many results reported in the literature on reverse-engineering this network are not significantly better than random. The discussion is further extended to some other networks commonly used for validation purposes in the literature. The results presented in this study emphasize that studies carried out using small genetic networks are likely to be trivial, making it imperative that larger real networks be used for validating and benchmarking purposes. If smaller networks are considered, then the results should be interpreted carefully to avoid over confidence. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.

  14. The development of hub architecture in the human functional brain network.

    PubMed

    Hwang, Kai; Hallquist, Michael N; Luna, Beatriz

    2013-10-01

    Functional hubs are brain regions that play a crucial role in facilitating communication among parallel, distributed brain networks. The developmental emergence and stability of hubs, however, is not well understood. The current study used measures of network topology drawn from graph theory to investigate the development of functional hubs in 99 participants, 10-20 years of age. We found that hub architecture was evident in late childhood and was stable from adolescence to early adulthood. Connectivity between hub and non-hub ("spoke") regions, however, changed with development. From childhood to adolescence, the strength of connections between frontal hubs and cortical and subcortical spoke regions increased. From adolescence to adulthood, hub-spoke connections with frontal hubs were stable, whereas connectivity between cerebellar hubs and cortical spoke regions increased. Our findings suggest that a developmentally stable functional hub architecture provides the foundation of information flow in the brain, whereas connections between hubs and spokes continue to develop, possibly supporting mature cognitive function.

  15. Low-Power Direct-Sequence Spread-Spectrum Modem Architecture for Distributed Wireless Sensor Networks

    SciTech Connect

    Chien, C; Elgorriaga, I; McConaghy, C

    2001-07-03

    Emerging CMOS and MEMS technologies enable the implementation of a large number of wireless distributed microsensors that can be easily and rapidly deployed to form highly redundant, self-configuring, and ad hoc sensor networks. To facilitate ease of deployment, these sensors should operate on battery for extended periods of time. A particular challenge in maintaining extended battery lifetime lies in achieving communications with low power. This paper presents a direct-sequence spread-spectrum modem architecture that provides robust communications for wireless sensor networks while dissipating very low power. The modem architecture has been verified in an FPGA implementation that dissipates only 33 mW for both transmission and reception. The implementation can be easily mapped to an ASIC technology, with an estimated power performance of less than 1 mW.

  16. A Hybrid Architecture of Neural Networks for Daily Streamflow Forecasting

    NASA Astrophysics Data System (ADS)

    Moradkhani, H.

    2001-12-01

    Streamflow forecasting has always been a challenging task for water resources engineers and managers and the major component of water resources system control. For years numerous techniques have been suggested and employed for streamflow forecasting. Computational Neural Networks (NNs), which are capable of recognizing hidden patterns in data, have recently become popular in many hydrologic applications. In this study, hybrid NN is developed for one step ahead forecasting of daily streamflow. Radial Basis Function (RBF) composed of a group of Gausian functions is used in conjunction with Self-Organizing Feature Map (SOFM) used in data classification. RBF transfers those classified input variables into the desired output estimate. Eight years of daily rainfall, streamflow, and temperature in Salt River basin were used for calibration and validation. Since 60%-80% of the water supply produced by the basin comes in the form of snow, further consideration of the existing time delay of snow melting process in the basin to the watershed outlet is important. Therefore two separated settings were considered in this simulation: the first one only includes several short-term daily rainfall and streamflow in the input sequence; the second setting, on the other hand, includes a longer time period (three-months) of temperature data sequence. Various statistical analyses, such as root mean square error, bias estimate, noise to signal ratio, and correlation coefficients of estimates and observations, were done to evaluate the forecast models. The preliminary results show that the accuracy of the model once considering the long-term effect of the snowmelt is conspicuous with respect to short-term effect. The effectiveness of the proposed and current operational models is evaluated.

  17. Optical pattern recognition architecture implementing the mean-square error correlation algorithm

    SciTech Connect

    Molley, P.A.

    1991-10-22

    This patent describes an optical architecture implementing the mean-square error correlation algorithm, MSE = {Sigma}(I {minus} R){sup 2} for discriminating the presence of a reference image R in an input image scene I by computing the mean-square-error between a time-varying reference image signal s{sub 1}(t) and a time-varying input image signal s{sub 2}(t) includes a laser diode light source which is temporally modulated by a double-sideband suppressed-carrier source modulation signal I{sub 1}(t) having the form I{sub 1}(t) = A{sub 1}(1 = sq. root 2m{sub 1}s{sub 1}(t)cos (2{pi} f{sub 0}t)) and the modulated light output from the laser diode source is diffracted by an acousto-optic deflector. The resultant intensity of the +1 diffracted order from the acousto-optic device is given by I{sub 2}(t) = A{sub 2}(+2m{sub 2}{sup 2}s{sub 2}{sup 2}(t) {minus} 2 sq. root 2m{sub 2}(t) cos (2{pi}f{sub 0}t)). The time integration of the two signals I{sub 1}(t) and I{sub 2}(t) on the CCD deflector plane produces the result R{tau} of the mean-square error having the form: R({tau}) = A{sub 1}A{sub 2}{l brace}(T) +(2m{sub 2}{sup 2 {integral} s}{sub 2}{sup 2}(t {minus} {tau})dt) {minus} (2m{sub 1}m{sub 2} cos (2{tau}f{sub 0}{tau}) {integral} s{sub 1}(t)s{sub 2}(t {minus} {tau}) dt){r brace}.

  18. A Common Bus In-Vehicle Network Architecture for Ground Army Vehicles

    DTIC Science & Technology

    2009-12-10

    CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Macam S Dattathreya 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT...application gateway, circuit level gateway, and proxy server. In addition to network firewalls, the software based program specific access authorizations...Oriented Architecture principle. They are developed using high level languages such as C++ or Java. The software components on the display devices provide

  19. The Architecture of Information Fusion System Ingreenhouse Wireless Sensor Network Based on Multi-Agent

    NASA Astrophysics Data System (ADS)

    Zhu, Wenting; Chen, Ming

    In view of current unprogressive situation of factory breeding in aquaculture, this article designed a standardized, informationized and intelligentized aquaculture system, proposed a information fusion architecture based on multi-agent in greenhouse wireless sensor network (GWSN), and researched mainly the structural characteristic of the four-classed information fusion based on distributed multi-agent and the method to construct the structure inside of every agent.

  20. Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications

    NASA Astrophysics Data System (ADS)

    Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min

    2015-12-01

    In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

  1. Principles of Network Architecture Emerging from Comparisons of the Cerebral Cortex in Large and Small Brains

    PubMed Central

    Finlay, Barbara L.

    2016-01-01

    The cerebral cortex retains its fundamental organization, layering, and input–output relations as it scales in volume over many orders of magnitude in mammals. How is its network architecture affected by size scaling? By comparing network organization of the mouse and rhesus macaque cortical connectome derived from complete neuroanatomical tracing studies, a recent study in PLOS Biology shows that an exponential distance rule emerges that reveals the falloff in connection probability with distance in the two brains that in turn determines common organizational features. PMID:27631433

  2. Architectural style classification of Mexican historical buildings using deep convolutional neural networks and sparse features

    NASA Astrophysics Data System (ADS)

    Obeso, Abraham Montoya; Benois-Pineau, Jenny; Acosta, Alejandro Álvaro Ramirez; Vázquez, Mireya Saraí García

    2017-01-01

    We propose a convolutional neural network to classify images of buildings using sparse features at the network's input in conjunction with primary color pixel values. As a result, a trained neuronal model is obtained to classify Mexican buildings in three classes according to the architectural styles: prehispanic, colonial, and modern with an accuracy of 88.01%. The problem of poor information in a training dataset is faced due to the unequal availability of cultural material. We propose a data augmentation and oversampling method to solve this problem. The results are encouraging and allow for prefiltering of the content in the search tasks.

  3. Novel photonic bandgap based architectures for quantum computers and networks

    NASA Astrophysics Data System (ADS)

    Guney, Durdu

    All of the approaches for quantum information processing have their own advantages, but unfortunately also their own drawbacks. Ideally, one would merge the most attractive features of those different approaches in a single technology. We envision that large-scale photonic crystal (PC) integrated circuits and fibers could be the basis for robust and compact quantum circuits and processors of the next generation quantum computers and networking devices. Cavity QED, solid-state, and (non)linear optical models for computing, and optical fiber approach for communications are the most promising candidates to be improved through this novel technology. In our work, we consider both digital and analog quantum computing. In the digital domain, we first perform gate-level analysis. To achieve this task, we solve the Jaynes-Cummings Hamiltonian with time-dependent coupling parameters under the dipole and rotating-wave approximations for a 3D PC single-mode cavity with a sufficiently high Q-factor. We then exploit the results to show how to create a maximally entangled state of two atoms and how to implement several quantum logic gates: a dual-rail Hadamard gate, a dual-rail NOT gate, and a SWAP gate. In all of these operations, we synchronize atoms, as opposed to previous studies with PCs. The method has the potential for extension to N-atom entanglement, universal quantum logic operations, and the implementation of other useful, cavity QED-based quantum information processing tasks. In the next part of the digital domain, we study circuit-level implementations. We design and simulate an integrated teleportation and readout circuit on a single PC chip. The readout part of our device can not only be used on its own but can also be integrated with other compatible optical circuits to achieve atomic state detection. Further improvement of the device in terms of compactness and robustness is possible by integrating with sources and detectors in the optical regime. In the analog

  4. Standard Spacecraft Interfaces and IP Network Architectures: Prototyping Activities at the GSFC

    NASA Technical Reports Server (NTRS)

    Schnurr, Richard; Marquart, Jane; Lin, Michael

    2003-01-01

    Advancements in fright semiconductor technology have opened the door for IP-based networking in spacecraft architectures. The GSFC believes the same signlJicant cost savings gained using MIL-STD-1553/1773 as a standard low rate interface for spacecraft busses cun be realized for highspeed network interfaces. To that end, GSFC is developing hardware and software to support a seamless, space mission IP network based on Ethernet and MIL-STD-1553. The Ethernet network shall connect all fright computers and communications systems using interface standards defined by the CCSDS Standard Onboard InterFace (SOIF) Panel. This paper shall discuss the prototyping effort underway at GSFC and expected results.

  5. Predicting electrocardiogram and arterial blood pressure waveforms with different Echo State Network architectures.

    PubMed

    Fong, Allan; Mittu, Ranjeev; Ratwani, Raj; Reggia, James

    2014-01-01

    Alarm fatigue caused by false alarms and alerts is an extremely important issue for the medical staff in Intensive Care Units. The ability to predict electrocardiogram and arterial blood pressure waveforms can potentially help the staff and hospital systems better classify a patient's waveforms and subsequent alarms. This paper explores the use of Echo State Networks, a specific type of neural network for mining, understanding, and predicting electrocardiogram and arterial blood pressure waveforms. Several network architectures are designed and evaluated. The results show the utility of these echo state networks, particularly ones with larger integrated reservoirs, for predicting electrocardiogram waveforms and the adaptability of such models across individuals. The work presented here offers a unique approach for understanding and predicting a patient's waveforms in order to potentially improve alarm generation. We conclude with a brief discussion of future extensions of this research.

  6. Architecture and statistical model of a pulse-mode digital multilayer neural network.

    PubMed

    Kim, Y C; Shanblatt, M A

    1995-01-01

    A new architecture and a statistical model for a pulse-mode digital multilayer neural network (DMNN) are presented. Algebraic neural operations are replaced by stochastic processes using pseudo-random pulse sequences. Synaptic weights and neuron states are represented as probabilities and estimated as average rates of pulse occurrences in corresponding pulse sequences. A statistical model of error (or noise) is developed to estimate relative accuracy associated with stochastic computing in terms of mean and variance. The stochastic computing technique is implemented with simple logic gates as basic computing elements leading to a high neuron-density on a chip. Furthermore, the use of simple logic gates for neural operations, the pulse-mode signal representation, and the modular design techniques lead to a massively parallel yet compact and flexible network architecture, well suited for VLSI implementation. Any size of a feedforward network can be configured where processing speed is independent of the network size. Multilayer feedforward networks are modeled and applied to pattern classification problems such as encoding and character recognition.

  7. Modeling workplace contact networks: The effects of organizational structure, architecture, and reporting errors on epidemic predictions

    PubMed Central

    Potter, Gail E.; Smieszek, Timo; Sailer, Kerstin

    2015-01-01

    Face-to-face social contacts are potentially important transmission routes for acute respiratory infections, and understanding the contact network can improve our ability to predict, contain, and control epidemics. Although workplaces are important settings for infectious disease transmission, few studies have collected workplace contact data and estimated workplace contact networks. We use contact diaries, architectural distance measures, and institutional structures to estimate social contact networks within a Swiss research institute. Some contact reports were inconsistent, indicating reporting errors. We adjust for this with a latent variable model, jointly estimating the true (unobserved) network of contacts and duration-specific reporting probabilities. We find that contact probability decreases with distance, and that research group membership, role, and shared projects are strongly predictive of contact patterns. Estimated reporting probabilities were low only for 0–5 min contacts. Adjusting for reporting error changed the estimate of the duration distribution, but did not change the estimates of covariate effects and had little effect on epidemic predictions. Our epidemic simulation study indicates that inclusion of network structure based on architectural and organizational structure data can improve the accuracy of epidemic forecasting models. PMID:26634122

  8. Strawberry Maturity Neural Network Detectng System Based on Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Xu, Liming

    The quick and non-detective detection of agriculture product is one of the measures to increase the precision and productivity of harvesting and grading. Having analyzed H frequency of different maturities in different light intensities, the results show that H frequency for the same maturity has little influence in different light intensities; Under the same light intensity, three strawberry maturities are changing in order. After having confirmed the H frequency section to distinguish the different strawberry maturity, the triplelayer feed-forward neural network system to detect strawberry maturity was designed by using genetic algorithm. The test results show that the detecting precision ratio is 91.7%, it takes 160ms to distinguish one strawberry. Therefore, the online non-detective detecting the strawberry maturity could be realized.

  9. Network intrusion detection by the coevolutionary immune algorithm of artificial immune systems with clonal selection

    NASA Astrophysics Data System (ADS)

    Salamatova, T.; Zhukov, V.

    2017-02-01

    The paper presents the application of the artificial immune systems apparatus as a heuristic method of network intrusion detection for algorithmic provision of intrusion detection systems. The coevolutionary immune algorithm of artificial immune systems with clonal selection was elaborated. In testing different datasets the empirical results of evaluation of the algorithm effectiveness were achieved. To identify the degree of efficiency the algorithm was compared with analogs. The fundamental rules based of solutions generated by this algorithm are described in the article.

  10. Network architecture design of an agile sensing system with sandwich wireless sensor nodes

    NASA Astrophysics Data System (ADS)

    Dorvash, S.; Li, X.; Pakzad, S.; Cheng, L.

    2012-04-01

    Wireless sensor network (WSN) is recently emerged as a powerful tool in the structural health monitoring (SHM). Due to the limitations of wireless channel capacity and the heavy data traffic, the control on the network is usually not real time. On the other hand, many SHM applications require quick response when unexpected events, such as earthquake, happen. Realizing the need to have an agile monitoring system, an approach, called sandwich node, was proposed. Sandwich is a design of complex sensor node where two Imote2 nodes are connected with each other to enhance the capabilities of the sensing units. The extra channel and processing power, added into the nodes, enable agile responses of the sensing network, particularly in interrupting the network and altering the undergoing tasks for burst events. This paper presents the design of a testbed for examination of the performance of wireless sandwich nodes in a network. The designed elements of the network are the software architecture of remote and local nodes, and the triggering strategies for coordinating the sensing units. The performance of the designed network is evaluated through its implementation in a monitoring test in the laboratory. For both original Imote2 and the sandwich node, the response time is estimated. The results show that the sandwich node is an efficient solution to the collision issue in existing interrupt approaches and the latency in dense wireless sensor networks.

  11. A cooperative positioning algorithm for DSRC enabled vehicular networks

    NASA Astrophysics Data System (ADS)

    Efatmaneshnik, M.; Kealy, A.; Alam, N.; Dempster, A. G.

    2011-12-01

    Many of the safety related applications that can be facilitated by Dedicated Short Range Communications (DSRC), such as vehicle proximity warnings, automated braking (e.g. at level crossings), speed advisories, pedestrian alerts etc., rely on a robust vehicle positioning capability such as that provided by a Global Navigation Satellite System (GNSS). Vehicles in remote areas, entering tunnels, high rise areas or any high multipath/ weak signal environment will challenge the integrity of GNSS position solutions, and ultimately the safety application it underpins. To address this challenge, this paper presents an innovative application of Cooperative Positioning techniques within vehicular networks. CP refers to any method of integrating measurements from different positioning systems and sensors in order to improve the overall quality (accuracy and reliability) of the final position solution. This paper investigates the potential of the DSRC infrastructure itself to provide an inter-vehicular ranging signal that can be used as a measurement within the CP algorithm. In this paper, time-based techniques of ranging are introduced and bandwidth requirements are investigated and presented. The robustness of the CP algorithm to inter-vehicle connection failure as well as GNSS dropouts is also demonstrated using simulation studies. Finally, the performance of the Constrained Kalman Filter used to integrate GNSS measurements with DSRC derived range estimates within a typical VANET is described and evaluated.

  12. Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks.

    PubMed

    Walter, Florian; Röhrbein, Florian; Knoll, Alois

    2015-12-01

    The application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high level of abstraction but employs highly realistic simulations of actual biological nervous systems. Even today, carrying out these simulations efficiently at appropriate timescales is challenging. Neuromorphic chip designs specially tailored to this task therefore offer an interesting perspective for neurorobotics. Unlike Von Neumann CPUs, these chips cannot be simply programmed with a standard programming language. Like real brains, their functionality is determined by the structure of neural connectivity and synaptic efficacies. Enabling higher cognitive functions for neurorobotics consequently requires the application of neurobiological learning algorithms to adjust synaptic weights in a biologically plausible way. In this paper, we therefore investigate how to program neuromorphic chips by means of learning. First, we provide an overview over selected neuromorphic chip designs and analyze them in terms of neural computation, communication systems and software infrastructure. On the theoretical side, we review neurobiological learning techniques. Based on this overview, we then examine on-die implementations of these learning algorithms on the considered neuromorphic chips. A final discussion puts the findings of this work into context and highlights how neuromorphic hardware can potentially advance the field of autonomous robot systems. The paper thus gives an in-depth overview of neuromorphic implementations of basic mechanisms of synaptic plasticity which are required to realize advanced cognitive capabilities with spiking neural networks.

  13. Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm

    PubMed Central

    Zheng, Ming; Wu, Jia-nan; Huang, Yan-xin; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang

    2012-01-01

    Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms. PMID:23226565

  14. Inferring gene regulatory networks by singular value decomposition and gravitation field algorithm.

    PubMed

    Zheng, Ming; Wu, Jia-nan; Huang, Yan-xin; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang

    2012-01-01

    Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms.

  15. Insights into Brain Architectures from the Homological Scaffolds of Functional Connectivity Networks

    PubMed Central

    Lord, Louis-David; Expert, Paul; Fernandes, Henrique M.; Petri, Giovanni; Van Hartevelt, Tim J.; Vaccarino, Francesco; Deco, Gustavo; Turkheimer, Federico; Kringelbach, Morten L.

    2016-01-01

    In recent years, the application of network analysis to neuroimaging data has provided useful insights about the brain's functional and structural organization in both health and disease. This has proven a significant paradigm shift from the study of individual brain regions in isolation. Graph-based models of the brain consist of vertices, which represent distinct brain areas, and edges which encode the presence (or absence) of a structural or functional relationship between each pair of vertices. By definition, any graph metric will be defined upon this dyadic representation of the brain activity. It is however unclear to what extent these dyadic relationships can capture the brain's complex functional architecture and the encoding of information in distributed networks. Moreover, because network representations of global brain activity are derived from measures that have a continuous response (i.e., interregional BOLD signals), it is methodologically complex to characterize the architecture of functional networks using traditional graph-based approaches. In the present study, we investigate the relationship between standard network metrics computed from dyadic interactions in a functional network, and a metric defined on the persistence homological scaffold of the network, which is a summary of the persistent homology structure of resting-state fMRI data. The persistence homological scaffold is a summary network that differs in important ways from the standard network representations of functional neuroimaging data: (i) it is constructed using the information from all edge weights comprised in the original network without applying an ad hoc threshold and (ii) as a summary of persistent homology, it considers the contributions of simplicial structures to the network organization rather than dyadic edge-vertices interactions. We investigated the information domain captured by the persistence homological scaffold by computing the strength of each node in the

  16. Development of Network-based Communications Architectures for Future NASA Missions

    NASA Technical Reports Server (NTRS)

    Slywczak, Richard A.

    2007-01-01

    Since the Vision for Space Exploration (VSE) announcement, NASA has been developing a communications infrastructure that combines existing terrestrial techniques with newer concepts and capabilities. The overall goal is to develop a flexible, modular, and extensible architecture that leverages and enhances terrestrial networking technologies that can either be directly applied or modified for the space regime. In addition, where existing technologies leaves gaps, new technologies must be developed. An example includes dynamic routing that accounts for constrained power and bandwidth environments. Using these enhanced technologies, NASA can develop nodes that provide characteristics, such as routing, store and forward, and access-on-demand capabilities. But with the development of the new infrastructure, challenges and obstacles will arise. The current communications infrastructure has been developed on a mission-by-mission basis rather than an end-to-end approach; this has led to a greater ground infrastructure, but has not encouraged communications between space-based assets. This alone provides one of the key challenges that NASA must encounter. With the development of the new Crew Exploration Vehicle (CEV), NASA has the opportunity to provide an integration path for the new vehicles and provide standards for their development. Some of the newer capabilities these vehicles could include are routing, security, and Software Defined Radios (SDRs). To meet these needs, the NASA/Glenn Research Center s (GRC) Network Emulation Laboratory (NEL) has been using both simulation and emulation to study and evaluate these architectures. These techniques provide options to NASA that directly impact architecture development. This paper identifies components of the infrastructure that play a pivotal role in the new NASA architecture, develops a scheme using simulation and emulation for testing these architectures and demonstrates how NASA can strengthen the new infrastructure by

  17. Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm.

    PubMed

    Ozturk, Celal; Karaboga, Dervis; Gorkemli, Beyza

    2011-01-01

    As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks.

  18. Ensuring Data Storage Security in Tree cast Routing Architecture for Sensor Networks

    NASA Astrophysics Data System (ADS)

    Kumar, K. E. Naresh; Sagar, U. Vidya; Waheed, Mohd. Abdul

    2010-10-01

    In this paper presents recent advances in technology have made low-cost, low-power wireless sensors with efficient energy consumption. A network of such nodes can coordinate among themselves for distributed sensing and processing of certain data. For which, we propose an architecture to provide a stateless solution in sensor networks for efficient routing in wireless sensor networks. This type of architecture is known as Tree Cast. We propose a unique method of address allocation, building up multiple disjoint trees which are geographically inter-twined and rooted at the data sink. Using these trees, routing messages to and from the sink node without maintaining any routing state in the sensor nodes is possible. In contrast to traditional solutions, where the IT services are under proper physical, logical and personnel controls, this routing architecture moves the application software and databases to the large data centers, where the management of the data and services may not be fully trustworthy. This unique attribute, however, poses many new security challenges which have not been well understood. In this paper, we focus on data storage security, which has always been an important aspect of quality of service. To ensure the correctness of users' data in this architecture, we propose an effective and flexible distributed scheme with two salient features, opposing to its predecessors. By utilizing the homomorphic token with distributed verification of erasure-coded data, our scheme achieves the integration of storage correctness insurance and data error localization, i.e., the identification of misbehaving server(s). Unlike most prior works, the new scheme further supports secure and efficient dynamic operations on data blocks, including: data update, delete and append. Extensive security and performance analysis shows that the proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and even server

  19. Computational performance comparison of wavefront reconstruction algorithms for the European Extremely Large Telescope on multi-CPU architecture.

    PubMed

    Feng, Lu; Fedrigo, Enrico; Béchet, Clémentine; Brunner, Elisabeth; Pirani, Werther

    2012-06-01

    The European Southern Observatory (ESO) is studying the next generation giant telescope, called the European Extremely Large Telescope (E-ELT). With a 42 m diameter primary mirror, it is a significant step from currently existing telescopes. Therefore, the E-ELT with its instruments poses new challenges in terms of cost and computational complexity for the control system, including its adaptive optics (AO). Since the conventional matrix-vector multiplication (MVM) method successfully used so far for AO wavefront reconstruction cannot be efficiently scaled to the size of the AO systems on the E-ELT, faster algorithms are needed. Among those recently developed wavefront reconstruction algorithms, three are studied in this paper from the point of view of design, implementation, and absolute speed on three multicore multi-CPU platforms. We focus on a single-conjugate AO system for the E-ELT. The algorithms are the MVM, the Fourier transform reconstructor (FTR), and the fractal iterative method (FRiM). This study enhances the scaling of these algorithms with an increasing number of CPUs involved in the computation. We discuss implementation strategies, depending on various CPU architecture constraints, and we present the first quantitative execution times so far at the E-ELT scale. MVM suffers from a large computational burden, making the current computing platform undersized to reach timings short enough for AO wavefront reconstruction. In our study, the FTR provides currently the fastest reconstruction. FRiM is a recently developed algorithm, and several strategies are investigated and presented here in order to implement it for real-time AO wavefront reconstruction, and to optimize its execution time. The difficulty to parallelize the algorithm in such architecture is enhanced. We also show that FRiM can provide interesting scalability using a sparse matrix approach.

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

    SciTech Connect

    Aiken, R.J.; Carlson, R.A.; Foster, I.T.

    1997-01-01

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

  1. Wireless Sensor Networks Approach

    NASA Technical Reports Server (NTRS)

    Perotti, Jose M.

    2003-01-01

    This viewgraph presentation provides information on hardware and software configurations for a network architecture for sensors. The hardware configuration uses a central station and remote stations. The software configuration uses the 'lost station' software algorithm. The presentation profiles a couple current examples of this network architecture in use.

  2. On the complexity of neural network classifiers: a comparison between shallow and deep architectures.

    PubMed

    Bianchini, Monica; Scarselli, Franco

    2014-08-01

    Recently, researchers in the artificial neural network field have focused their attention on connectionist models composed by several hidden layers. In fact, experimental results and heuristic considerations suggest that deep architectures are more suitable than shallow ones for modern applications, facing very complex problems, e.g., vision and human language understanding. However, the actual theoretical results supporting such a claim are still few and incomplete. In this paper, we propose a new approach to study how the depth of feedforward neural networks impacts on their ability in implementing high complexity functions. First, a new measure based on topological concepts is introduced, aimed at evaluating the complexity of the function implemented by a neural network, used for classification purposes. Then, deep and shallow neural architectures with common sigmoidal activation functions are compared, by deriving upper and lower bounds on their complexity, and studying how the complexity depends on the number of hidden units and the used activation function. The obtained results seem to support the idea that deep networks actually implements functions of higher complexity, so that they are able, with the same number of resources, to address more difficult problems.

  3. Spatially constrained adaptive rewiring in cortical networks creates spatially modular small world architectures.

    PubMed

    Jarman, Nicholas; Trengove, Chris; Steur, Erik; Tyukin, Ivan; van Leeuwen, Cees

    2014-12-01

    A modular small-world topology in functional and anatomical networks of the cortex is eminently suitable as an information processing architecture. This structure was shown in model studies to arise adaptively; it emerges through rewiring of network connections according to patterns of synchrony in ongoing oscillatory neural activity. However, in order to improve the applicability of such models to the cortex, spatial characteristics of cortical connectivity need to be respected, which were previously neglected. For this purpose we consider networks endowed with a metric by embedding them into a physical space. We provide an adaptive rewiring model with a spatial distance function and a corresponding spatially local rewiring bias. The spatially constrained adaptive rewiring principle is able to steer the evolving network topology to small world status, even more consistently so than without spatial constraints. Locally biased adaptive rewiring results in a spatial layout of the connectivity structure, in which topologically segregated modules correspond to spatially segregated regions, and these regions are linked by long-range connections. The principle of locally biased adaptive rewiring, thus, may explain both the topological connectivity structure and spatial distribution of connections between neuronal units in a large-scale cortical architecture.

  4. To Propose a Reviewer Dispatching Algorithm for Networked Peer Assessment System.

    ERIC Educational Resources Information Center

    Liu, Eric Zhi-Feng

    2005-01-01

    Despite their increasing availability on the Internet, networked peer assessment systems (1-5) lack feasible automatic dispatching algorithm of student's assignments and ultimately inhibit the effectiveness of peer assessment. Therefore, this study presents a reviewer dispatching algorithm capable of supporting networked peer assessment system in…

  5. A Fast and Efficient Algorithm for Mining Top-k Nodes in Complex Networks

    NASA Astrophysics Data System (ADS)

    Liu, Dong; Jing, Yun; Zhao, Jing; Wang, Wenjun; Song, Guojie

    2017-02-01

    One of the key problems in social network analysis is influence maximization, which has great significance both in theory and practical applications. Given a complex network and a positive integer k, and asks the k nodes to trigger the largest expected number of the remaining nodes. Many mature algorithms are mainly divided into propagation-based algorithms and topology- based algorithms. The propagation-based algorithms are based on optimization of influence spread process, so the influence spread of them significantly outperforms the topology-based algorithms. But these algorithms still takes days to complete on large networks. Contrary to propagation based algorithms, the topology-based algorithms are based on intuitive parameter statistics and static topology structure properties. Their running time are extremely short but the results of influence spread are unstable. In this paper, we propose a novel topology-based algorithm based on local index rank (LIR). The influence spread of our algorithm is close to the propagation-based algorithm and sometimes over them. Moreover, the running time of our algorithm is millions of times shorter than that of propagation-based algorithms. Our experimental results show that our algorithm has a good and stable performance in IC and LT model.

  6. A Fast and Efficient Algorithm for Mining Top-k Nodes in Complex Networks.

    PubMed

    Liu, Dong; Jing, Yun; Zhao, Jing; Wang, Wenjun; Song, Guojie

    2017-02-27

    One of the key problems in social network analysis is influence maximization, which has great significance both in theory and practical applications. Given a complex network and a positive integer k, and asks the k nodes to trigger the largest expected number of the remaining nodes. Many mature algorithms are mainly divided into propagation-based algorithms and topology- based algorithms. The propagation-based algorithms are based on optimization of influence spread process, so the influence spread of them significantly outperforms the topology-based algorithms. But these algorithms still takes days to complete on large networks. Contrary to propagation based algorithms, the topology-based algorithms are based on intuitive parameter statistics and static topology structure properties. Their running time are extremely short but the results of influence spread are unstable. In this paper, we propose a novel topology-based algorithm based on local index rank (LIR). The influence spread of our algorithm is close to the propagation-based algorithm and sometimes over them. Moreover, the running time of our algorithm is millions of times shorter than that of propagation-based algorithms. Our experimental results show that our algorithm has a good and stable performance in IC and LT model.

  7. A Fast and Efficient Algorithm for Mining Top-k Nodes in Complex Networks

    PubMed Central

    Liu, Dong; Jing, Yun; Zhao, Jing; Wang, Wenjun; Song, Guojie

    2017-01-01

    One of the key problems in social network analysis is influence maximization, which has great significance both in theory and practical applications. Given a complex network and a positive integer k, and asks the k nodes to trigger the largest expected number of the remaining nodes. Many mature algorithms are mainly divided into propagation-based algorithms and topology- based algorithms. The propagation-based algorithms are based on optimization of influence spread process, so the influence spread of them significantly outperforms the topology-based algorithms. But these algorithms still takes days to complete on large networks. Contrary to propagation based algorithms, the topology-based algorithms are based on intuitive parameter statistics and static topology structure properties. Their running time are extremely short but the results of influence spread are unstable. In this paper, we propose a novel topology-based algorithm based on local index rank (LIR). The influence spread of our algorithm is close to the propagation-based algorithm and sometimes over them. Moreover, the running time of our algorithm is millions of times shorter than that of propagation-based algorithms. Our experimental results show that our algorithm has a good and stable performance in IC and LT model. PMID:28240238

  8. Binary tree-based fault location algorithm for optical burst switching network

    NASA Astrophysics Data System (ADS)

    Wang, Ru-Yan; Liu, Dan; Peng, Huan-Jia; Lv, Ke-Wei

    2009-07-01

    This paper proposes an effective method of fault location based on a binary tree for optical burst switching (OBS) network. To minimize the monitoring cost, we divide the network into several monitor domains by introducing monitoring-cycle algorithms. In order to generate an exclusive code, we modify the monitoring cycle algorithm when two nodes have the same code. Through the binary tree algorithm, a pre-computation of faults in the OBS network can be achieved. When a fault happens, we can locate it immediately and accurately. Examples have proved that the algorithm has general applicability.

  9. Optical pattern recognition architecture implementing the mean-square error correlation algorithm

    DOEpatents

    Molley, Perry A.

    1991-01-01

    An optical architecture implementing the mean-square error correlation algorithm, MSE=.SIGMA.[I-R].sup.2 for discriminating the presence of a reference image R in an input image scene I by computing the mean-square-error between a time-varying reference image signal s.sub.1 (t) and a time-varying input image signal s.sub.2 (t) includes a laser diode light source which is temporally modulated by a double-sideband suppressed-carrier source modulation signal I.sub.1 (t) having the form I.sub.1 (t)=A.sub.1 [1+.sqroot.2m.sub.1 s.sub.1 (t)cos (2.pi.f.sub.o t)] and the modulated light output from the laser diode source is diffracted by an acousto-optic deflector. The resultant intensity of the +1 diffracted order from the acousto-optic device is given by: I.sub.2 (t)=A.sub.2 [+2m.sub.2.sup.2 s.sub.2.sup.2 (t)-2.sqroot.2m.sub.2 (t) cos (2.pi.f.sub.o t] The time integration of the two signals I.sub.1 (t) and I.sub.2 (t) on the CCD deflector plane produces the result R(.tau.) of the mean-square error having the form: R(.tau.)=A.sub.1 A.sub.2 {[T]+[2m.sub.2.sup.2.multidot..intg.s.sub.2.sup.2 (t-.tau.)dt]-[2m.sub.1 m.sub.2 cos (2.tau.f.sub.o .tau.).multidot..intg.s.sub.1 (t)s.sub.2 (t-.tau.)dt]} where: s.sub.1 (t) is the signal input to the diode modulation source: s.sub.2 (t) is the signal input to the AOD modulation source; A.sub.1 is the light intensity; A.sub.2 is the diffraction efficiency; m.sub.1 and m.sub.2 are constants that determine the signal-to-bias ratio; f.sub.o is the frequency offset between the oscillator at f.sub.c and the modulation at f.sub.c +f.sub.o ; and a.sub.o and a.sub.1 are constant chosen to bias the diode source and the acousto-optic deflector into their respective linear operating regions so that the diode source exhibits a linear intensity characteristic and the AOD exhibits a linear amplitude characteristic.

  10. Storage capacity and learning algorithms for two-layer neural networks

    NASA Astrophysics Data System (ADS)

    Engel, A.; Köhler, H. M.; Tschepke, F.; Vollmayr, H.; Zippelius, A.

    1992-05-01

    A two-layer feedforward network of McCulloch-Pitts neurons with N inputs and K hidden units is analyzed for N-->∞ and K finite with respect to its ability to implement p=αN random input-output relations. Special emphasis is put on the case where all hidden units are coupled to the output with the same strength (committee machine) and the receptive fields of the hidden units either enclose all input units (fully connected) or are nonoverlapping (tree structure). The storage capacity is determined generalizing Gardner's treatment [J. Phys. A 21, 257 (1988); Europhys. Lett. 4, 481 (1987)] of the single-layer perceptron. For the treelike architecture, a replica-symmetric calculation yields αc~ √K for a large number K of hidden units. This result violates an upper bound derived by Mitchison and Durbin [Biol. Cybern. 60, 345 (1989)]. One-step replica-symmetry breaking gives lower values of αc. In the fully connected committee machine there are in general correlations among different hidden units. As the limit of capacity is approached, the hidden units are anticorrelated: One hidden unit attempts to learn those patterns which have not been learned by the others. These correlations decrease as 1/K, so that for K-->∞ the capacity per synapse is the same as for the tree architecture, whereas for small K we find a considerable enhancement for the storage per synapse. Numerical simulations were performed to explicitly construct solutions for the tree as well as the fully connected architecture. A learning algorithm is suggested. It is based on the least-action algorithm, which is modified to take advantage of the two-layer structure. The numerical simulations yield capacities p that are slightly more than twice the number of degrees of freedom, while the fully connected net can store relatively more patterns than the tree. Various generalizations are discussed. Variable weights from hidden to output give the same results for the storage capacity as does the committee

  11. An Optimal Schedule for Urban Road Network Repair Based on the Greedy Algorithm

    PubMed Central

    Lu, Guangquan; Xiong, Ying; Wang, Yunpeng

    2016-01-01

    The schedule of urban road network recovery caused by rainstorms, snow, and other bad weather conditions, traffic incidents, and other daily events is essential. However, limited studies have been conducted to investigate this problem. We fill this research gap by proposing an optimal schedule for urban road network repair with limited repair resources based on the greedy algorithm. Critical links will be given priority in repair according to the basic concept of the greedy algorithm. In this study, the link whose restoration produces the ratio of the system-wide travel time of the current network to the worst network is the minimum. We define such a link as the critical link for the current network. We will re-evaluate the importance of damaged links after each repair process is completed. That is, the critical link ranking will be changed along with the repair process because of the interaction among links. We repair the most critical link for the specific network state based on the greedy algorithm to obtain the optimal schedule. The algorithm can still quickly obtain an optimal schedule even if the scale of the road network is large because the greedy algorithm can reduce computational complexity. We prove that the problem can obtain the optimal solution using the greedy algorithm in theory. The algorithm is also demonstrated in the Sioux Falls network. The problem discussed in this paper is highly significant in dealing with urban road network restoration. PMID:27768732

  12. A Parallel Algorithm for Connected Component Labelling of Gray-scale Images on Homogeneous Multicore Architectures

    NASA Astrophysics Data System (ADS)

    Niknam, Mehdi; Thulasiraman, Parimala; Camorlinga, Sergio

    2010-11-01

    Connected component labelling is an essential step in image processing. We provide a parallel version of Suzuki's sequential connected component algorithm in order to speed up the labelling process. Also, we modify the algorithm to enable labelling gray-scale images. Due to the data dependencies in the algorithm we used a method similar to pipeline to exploit parallelism. The parallel algorithm method achieved a speedup of 2.5 for image size of 256 × 256 pixels using 4 processing threads.

  13. A Hybrid Adaptive Routing Algorithm for Event-Driven Wireless Sensor Networks

    PubMed Central

    Figueiredo, Carlos M. S.; Nakamura, Eduardo F.; Loureiro, Antonio A. F.

    2009-01-01

    Routing is a basic function in wireless sensor networks (WSNs). For these networks, routing algorithms depend on the characteristics of the applications and, consequently, there is no self-contained algorithm suitable for every case. In some scenarios, the network behavior (traffic load) may vary a lot, such as an event-driven application, favoring different algorithms at different instants. This work presents a hybrid and adaptive algorithm for routing in WSNs, called Multi-MAF, that adapts its behavior autonomously in response to the variation of network conditions. In particular, the proposed algorithm applies both reactive and proactive strategies for routing infrastructure creation, and uses an event-detection estimation model to change between the strategies and save energy. To show the advantages of the proposed approach, it is evaluated through simulations. Comparisons with independent reactive and proactive algorithms show improvements on energy consumption. PMID:22423207

  14. Beyond 'furballs' and 'dumpling soups' - towards a molecular architecture of signaling complexes and networks.

    PubMed

    Lewitzky, Marc; Simister, Philip C; Feller, Stephan M

    2012-08-14

    The molecular architectures of intracellular signaling networks are largely unknown. Understanding their design principles and mechanisms of processing information is essential to grasp the molecular basis of virtually all biological processes. This is particularly challenging for human pathologies like cancers, as essentially each tumor is a unique disease with vastly deranged signaling networks. However, even in normal cells we know almost nothing. A few 'signalosomes', like the COP9 and the TCR signaling complexes have been described, but detailed structural information on their architectures is largely lacking. Similarly, many growth factor receptors, for example EGF receptor, insulin receptor and c-Met, signal via huge protein complexes built on large platform proteins (Gab, Irs/Dok, p130Cas[BCAR1], Frs families etc.), which are structurally not well understood. Subsequent higher order processing events remain even more enigmatic. We discuss here methods that can be employed to study signaling architectures, and the importance of too often neglected features like macromolecular crowding, intrinsic disorder in proteins and the sophisticated cellular infrastructures, which need to be carefully considered in order to develop a more mature understanding of cellular signal processing.

  15. GENNET-Toolbox: An Evolving Genetic Algorithm for Neural Network Training

    NASA Astrophysics Data System (ADS)

    Gómez-Garay, Vicente; Irigoyen, Eloy; Artaza, Fernando

    Genetic Algorithms have been used from 1989 for both Neural Network training and design. Nevertheless, the use of a Genetic Algorithm for adjusting the Neural Network parameters can still be engaging. This work presents the study and validation of a different approach to this matter by introducing a Genetic Algorithm designed for Neural Network training. This algorithm features a mutation operator capable of working on three levels (network, neuron and layer) and with the mutation parameters encoded and evolving within each individual. We also explore the use of three types of hybridization: post-training, Lamarckian and Baldwinian. These proposes in combination with the algorithm, show for a fast and powerful tool for Neural Network training.

  16. A New Node Deployment and Location Dispatch Algorithm for Underwater Sensor Networks.

    PubMed

    Jiang, Peng; Liu, Jun; Ruan, Binfeng; Jiang, Lurong; Wu, Feng

    2016-01-09

    Considering that deployment strategies for underwater sensor networks should contribute to fully connecting the networks, a Guaranteed Full Connectivity Node Deployment (GFCND) algorithm is proposed in this study. The GFCND algorithm attempts to deploy the coverage nodes according to the greedy iterative strategy, after which the connectivity nodes are used to improve network connectivity and fully connect the whole network. Furthermore, a Location Dispatch Based on Command Nodes (LDBCN) algorithm is proposed, which accomplishes the location adjustment of the common nodes with the help of the SINK node and the command nodes. The command nodes then dispatch the common nodes. Simulation results show that the GFCND algorithm achieves a comparatively large coverage percentage and a fully connected network; furthermore, the LDBCN algorithm helps the common nodes preserve more total energy when they reach their destination locations.

  17. A service-oriented architecture for integrating the modeling and formal verification of genetic regulatory networks

    PubMed Central

    2009-01-01

    Background The study of biological networks has led to the development of increasingly large and detailed models. Computer tools are essential for the simulation of the dynamical behavior of the networks from the model. However, as the size of the models grows, it becomes infeasible to manually verify the predictions against experimental data or identify interesting features in a large number of simulation traces. Formal verification based on temporal logic and model checking provides promising methods to automate and scale the analysis of the models. However, a framework that tightly integrates modeling and simulation tools with model checkers is currently missing, on both the conceptual and the implementational level. Results We have developed a generic and modular web service, based on a service-oriented architecture, for integrating the modeling and formal verification of genetic regulatory networks. The architecture has been implemented in the context of the qualitative modeling and simulation tool GNA and the model checkers NUSMV and CADP. GNA has been extended with a verification module for the specification and checking of biological properties. The verification module also allows the display and visual inspection of the verification results. Conclusions The practical use of the proposed web service is illustrated by means of a scenario involving the analysis of a qualitative model of the carbon starvation response in E. coli. The service-oriented architecture allows modelers to define the model and proceed with the specification and formal verification of the biological properties by means of a unified graphical user interface. This guarantees a transparent access to formal verification technology for modelers of genetic regulatory networks. PMID:20042075

  18. The dynamic nature of mollusc egg surface architecture and its relation to the microtubule network.

    PubMed

    Tyler, Sheena E B; Kimber, Susan J

    2006-01-01

    Dynamic changes in the surface architecture pattern of embryos of the slipper limpet (Crepidula fornicata, Mollusca) were found in this study to correlate with the dynamic activity and pattern of the underlying mitotic spindle microtubule network, revealed by fluorescent labelling and confocal imaging techniques. Examination of a series of optical sections indicate that this network appears to be spatially co-ordinated together as a whole throughout the embryo. The microtubule pattern also associated with abnormal multipolar spindles resulting from an applied static magnetic field, indicating that the pattern may be generated by a natural endogenous field source. The patterning characteristics of the surface and microtubule network together provide further morphological evidence for a primary morphogenetic or developmental field system which organises the primary body axis and co-ordinates the pattern of cleavage.

  19. Improved passive optical network architectures to support local area network emulation and protection

    NASA Astrophysics Data System (ADS)

    Wong, Elaine; Nadarajah, Nishaanthan; Chae, Chang-Joon; Nirmalathas, Ampalavanapillai; Attygalle, Sanjeewa M.

    2006-01-01

    We describe two optical layer schemes which simultaneously facilitate local area network emulation and automatic protection switching against distribution fiber breaks in passive optical networks. One scheme employs a narrowband fiber Bragg grating placed close to the star coupler in the feeder fiber of the passive optical network, while the other uses an additional short length distribution fiber from the star coupler to each customer for the redirection of the customer traffic. Both schemes use RF subcarrier multiplexed transmission for intercommunication between customers in conjunction with upstream access to the central office at baseband. Failure detection and automatic protection switching are performed independently by each optical network unit that is located at the customer premises in a distributed manner. The restoration of traffic transported between the central office and an optical network unit in the event of the distribution fiber break is performed by interconnecting adjacent optical network units and carrying out signal transmissions via an independent but interconnected optical network unit. Such a protection mechanism enables multiple adjacent optical network units to be simultaneously protected by a single optical network unit utilizing its maximum available bandwidth. We experimentally verify the feasibility of both schemes with 1.25 Gb/s upstream baseband transmission to the central office and 155 Mb/s local area network data transmission on a RF subcarrier frequency. The experimental results obtained from both schemes are compared, and the power budgets are calculated to analyze the scalability of each scheme.

  20. A low-jitter and high-throughput scheduling based on genetic algorithm in slotted WDM networks

    NASA Astrophysics Data System (ADS)

    Zhang, Jingjing; Jin, Yaohui; Su, Yikai; Xu, Buwei; Zhang, Chunlei; Zhu, Yi; Hu, Weisheng

    2005-02-01

    Slotted WDM, which achieves higher capacity compared with conventional WDM and SDH networks, has been discussed a lot recently. The ring network for this architecture has been demonstrated experimentally. In slotted WDM ring network, each node is equipped with a wavelength-tunable transmitter and a fixed receiver and assigned with a specific wavelength. A node can send data to every other node by tuning wavelength accordingly in a time slot. One of the important issues for it is scheduling. Scheduling of it can be reduced to input queued switch when synchronization and propagation are solved and many schemes have been proposed to solve these two issues. However, it"s proved that scheduling of such a network taking both jitter and throughput into consideration is NP hard. Greedy algorithm has been proposed to solve it before. The main contribution of this paper lies in a novel genetic algorithm to obtain optimal or near optimal value of this specific NP hard problem. We devise problem specific chromosome codes, fitness function, crossover and mutation operations. Experimental results show that our GA provides better performances in terms of throughput and jitter than a greedy heuristic.