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Sample records for algorithms network architectures

  1. Neural-network algorithms and architectures for pattern classification

    SciTech Connect

    Mao, Weidong.

    1991-01-01

    The study of the artificial neural networks is an integrated research field that involves the disciplines of applied mathematics, physics, neurobiology, computer science, information, control, parallel processing and VLSI. This dissertation deals with a number of topics from a broad spectrum of neural network research in models, algorithms, applications and VLSI architectures. Specifically, this dissertation is aimed at studying neural network algorithms and architectures for pattern classification tasks. The work presented in this dissertation has a wide range of applications including speech recognition, image recognition, and high level knowledge processing. Supervised neural networks, such as the back-propagation network, can be used for classification tasks as the result of approximating an input/output mapping. They are the approximation-based classifiers. The original gradient descent back propagation learning algorithm exhibits slow convergence speed. Fast algorithms such as the conjugate gradient and quasi-Newton algorithms can be adopted. The main emphasis on neural network classifiers in this dissertation is the competition-based classifiers. Due to the rapid advance in VLSI technology, parallel processing, and computer aided design (CAD), application-specific VLSI systems are becoming more and more powerful and feasible. In particular, VLSI array processors offer high speed and efficiency through their massive parallelism and pipelining, regularity, modularity, and local communication. A unified VLSI array architecture can be used for implementing neural networks and Hidden Markov Models. He also proposes a pipeline interleaving approach to design VLSI array architectures for real-time image and video signal processing.

  2. 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.

  3. Architecture for High Speed Learning of Neural Network using Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Yoshikawa, Masaya; Terai, Hidekazu

    This paper discusses the architecture for high speed learning of Neural Network (NN) using Genetic Algorithm (GA). The proposed architecture prevents local minimum by using the GA characteristic of holding several individual populations for a population-based search and achieves high speed processing adopting dedicated hardware. To keep general purpose equal software processing, the proposed architecture can be flexible genetic operations on GA and is introduced both Sigmoid function and Heaviside function on NN. Furthermore, the proposed architecture is not optimized only the pipeline at evaluation phase on NN, but also optimized hierarchic pipelines on the whole at evolutionary phase. We have done the simulation, verification and logic synthesis using library of 0.35μm CMOS standard cell. Simulation results evaluating the proposed architecture show to achieve 22 times speed on average compared with software processing.

  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. Routing and wavelength assignment algorithms for all-optical WDM networks based on virtual multiple self-healing ring architecture

    NASA Astrophysics Data System (ADS)

    Ishikawa, Akio; Kishi, Yoji

    2000-09-01

    This paper newly proposes a self-healing architecture in all- optical WDM networks based on virtual embedded multiple rings (Virtual Multiple Self Healing Rings: VM-SHR). Focusing upon the network design aspect of the proposed architecture, this paper describes design methodologies for VM-SHR networks. For two major problems in all-optical WDM network design, that is, the connection routing and wavelength assignment problems, we first established solution models based on mathematical programming formulation, each of which can be solved by common integer programming algorithms, respectively. In addition, we also developed an efficient heuristic algorithm for the wavelength assignment problem. Their usefulness and performance are demonstrated through the extensive simulation results.

  6. Parallel algorithms and architectures

    SciTech Connect

    Albrecht, A.; Jung, H.; Mehlhorn, K.

    1987-01-01

    Contents of this book are the following: Preparata: Deterministic simulation of idealized parallel computers on more realistic ones; Convex hull of randomly chosen points from a polytope; Dataflow computing; Parallel in sequence; Towards the architecture of an elementary cortical processor; Parallel algorithms and static analysis of parallel programs; Parallel processing of combinatorial search; Communications; An O(nlogn) cost parallel algorithms for the single function coarsest partition problem; Systolic algorithms for computing the visibility polygon and triangulation of a polygonal region; and RELACS - A recursive layout computing system. Parallel linear conflict-free subtree access.

  7. Quantifying Loopy Network Architectures

    PubMed Central

    Katifori, Eleni; Magnasco, Marcelo O.

    2012-01-01

    Biology presents many examples of planar distribution and structural networks having dense sets of closed loops. An archetype of this form of network organization is the vasculature of dicotyledonous leaves, which showcases a hierarchically-nested architecture containing closed loops at many different levels. Although a number of approaches have been proposed to measure aspects of the structure of such networks, a robust metric to quantify their hierarchical organization is still lacking. We present an algorithmic framework, the hierarchical loop decomposition, that allows mapping loopy networks to binary trees, preserving in the connectivity of the trees the architecture of the original graph. We apply this framework to investigate computer generated graphs, such as artificial models and optimal distribution networks, as well as natural graphs extracted from digitized images of dicotyledonous leaves and vasculature of rat cerebral neocortex. We calculate various metrics based on the asymmetry, the cumulative size distribution and the Strahler bifurcation ratios of the corresponding trees and discuss the relationship of these quantities to the architectural organization of the original graphs. This algorithmic framework decouples the geometric information (exact location of edges and nodes) from the metric topology (connectivity and edge weight) and it ultimately allows us to perform a quantitative statistical comparison between predictions of theoretical models and naturally occurring loopy graphs. PMID:22701593

  8. UMTS network architecture

    NASA Astrophysics Data System (ADS)

    Katoen, J. P.; Saiedi, A.; Baccaro, I.

    1994-05-01

    This paper proposes a Functional Architecture and a corresponding Network Architecture for the Universal Mobile Telecommunication System (UMTS). Procedures like call handling, location management, and handover are considered. The architecture covers the domestic, business, and public environments. Integration with existing and forthcoming networks for fixed communications is anticipated and the Intelligent Network (IN) philosophy is applied.

  9. FTS2000 network architecture

    NASA Technical Reports Server (NTRS)

    Klenart, John

    1991-01-01

    The network architecture of FTS2000 is graphically depicted. A map of network A topology is provided, with interservice nodes. Next, the four basic element of the architecture is laid out. Then, the FTS2000 time line is reproduced. A list of equipment supporting FTS2000 dedicated transmissions is given. Finally, access alternatives are shown.

  10. 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…

  11. MSAT network architecture

    NASA Technical Reports Server (NTRS)

    Davies, N. G.; Skerry, B.

    1990-01-01

    The Mobile Satellite (MSAT) communications system will support mobile voice and data services using circuit switched and packet switched facilities with interconnection to the public switched telephone network and private networks. Control of the satellite network will reside in a Network Control System (NCS) which is being designed to be extremely flexible to provide for the operation of the system initially with one multi-beam satellite, but with capability to add additional satellites which may have other beam configurations. The architecture of the NCS is described. The signalling system must be capable of supporting the protocols for the assignment of circuits for mobile public telephone and private network calls as well as identifying packet data networks. The structure of a straw-man signalling system is discussed.

  12. Development of the neural network algorithm projecting system Neural Architecture and its application in combining medical expert systems

    NASA Astrophysics Data System (ADS)

    Timofeew, Sergey; Eliseev, Vladimir; Tcherkassov, Oleg; Birukow, Valentin; Orbachevskyi, Leonid; Shamsutdinov, Uriy

    1998-04-01

    Some problems of creation of medical expert systems and the ways of their overcoming using artificial neural networks are discussed. The instrumental system for projecting neural network algorithms `Neural Architector', developed by the authors, is described. It allows to perform effective modeling of artificial neural networks and to analyze their work. The example of the application of the `Neural Architector' system in composing an expert system for diagnostics of pulmonological diseases is shown.

  13. Constructive neural network learning algorithms

    SciTech Connect

    Parekh, R.; Yang, Jihoon; Honavar, V.

    1996-12-31

    Constructive Algorithms offer an approach for incremental construction of potentially minimal neural network architectures for pattern classification tasks. These algorithms obviate the need for an ad-hoc a-priori choice of the network topology. The constructive algorithm design involves alternately augmenting the existing network topology by adding one or more threshold logic units and training the newly added threshold neuron(s) using a stable variant of the perception learning algorithm (e.g., pocket algorithm, thermal perception, and barycentric correction procedure). Several constructive algorithms including tower, pyramid, tiling, upstart, and perception cascade have been proposed for 2-category pattern classification. These algorithms differ in terms of their topological and connectivity constraints as well as the training strategies used for individual neurons.

  14. 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).

  15. Architectural Implications for Spatial Object Association Algorithms*

    PubMed Central

    Kumar, Vijay S.; Kurc, Tahsin; Saltz, Joel; Abdulla, Ghaleb; Kohn, Scott R.; Matarazzo, Celeste

    2013-01-01

    Spatial object association, also referred to as crossmatch 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®, 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). PMID:25692244

  16. 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.

  17. Neural algorithms on VLSI concurrent architectures

    SciTech Connect

    Caviglia, D.D.; Bisio, G.M.; Parodi, G.

    1988-09-01

    The research concerns the study of neural algorithms for developing CAD tools with A.I. features in VLSI design activities. In this paper the focus is on optimization problems such as partitioning, placement and routing. These problems require massive computational power to be solved (NP-complete problems) and the standard approach is usually based on euristic techniques. Neural algorithms can be represented by a circuital model. This kind of representation can be easily mapped in a real circuit, which, however, features limited flexibility with respect to the variety of problems. In this sense the simulation of the neural circuit, by mapping it on a digital VLSI concurrent architecture seems to be preferrable; in addition this solution offers a wider choice with regard to algorithms characteristics (e.g. transfer curve of neural elements, reconfigurability of interconnections, etc.). The implementation with programmable components, such as transputers, allows an indirect mapping of the algorithm (one transputer for N neurons) accordingly to the dimension and the characteristics of the problem. In this way the neural algorithm described by the circuit is reduced to the algorithm that simulates the network behavior. The convergence properties of that formulation are studied with respect to the characteristics of the neural element transfer curve.

  18. 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

  19. ATMTN: a telemammography network architecture.

    PubMed

    Sheybani, Ehsan O; Sankar, Ravi

    2002-12-01

    One of the goals of the National Cancer Institute (NCI) to reach more than 80% of eligible women in mammography screening by the year 2000 yet remains as a challenge. In fact, a recent medical report reveals that while other types of cancer are experiencing negative growth, breast cancer has been the only one with a positive growth rate over the last few years. This is primarily due to the fact that 1) examination process is a complex and lengthy one and 2) it is not available to the majority of women who live in remote sites. Currently for mammography screening, women have to go to doctors or cancer centers/hospitals annually while high-risk patients may have to visit more often. One way to resolve these problems is by the use of advanced networking technologies and signal processing algorithms. On one hand, software modules can help detect, with high precision, true negatives (TN), while marking true positives (TP) for further investigation. Unavoidably, in this process some false negatives (FN) will be generated that are potentially life threatening; however, inclusion of the detection software improves the TP detection and, hence, reduces FNs drastically. Since TNs are the majority of examinations on a randomly selected population, this first step reduces the load on radiologists by a tremendous amount. On the other hand, high-speed networking equipment can accelerate the required clinic-lab connection and make detection, segmentation, and image enhancement algorithms readily available to the radiologists. This will bring the breast cancer care, caregiver, and the facilities to the patients and expand diagnostics and treatment to the remote sites. This research describes asynchronous transfer mode telemammography network (ATMTN) architecture for real-time, online screening, detection and diagnosis of breast cancer. ATMTN is a unique high-speed network integrated with automatic robust computer-assisted diagnosis-detection/digital signal processing (CAD

  20. Unifying parametrized VLSI Jacobi algorithms and architectures

    NASA Astrophysics Data System (ADS)

    Deprettere, Ed F. A.; Moonen, Marc

    1993-11-01

    Implementing Jacobi algorithms in parallel VLSI processor arrays is a non-trivial task, in particular when the algorithms are parametrized with respect to size and the architectures are parametrized with respect to space-time trade-offs. The paper is concerned with an approach to implement several time-adaptive Jacobi-type algorithms on a parallel processor array, using only Cordic arithmetic and asynchronous communications, such that any degree of parallelism, ranging from single-processor up to full-size array implementation, is supported by a `universal' processing unit. This result is attributed to a gracious interplay between algorithmic and architectural engineering.

  1. Introduction to systolic algorithms and architectures

    SciTech Connect

    Bentley, J.L.; Kung, H.T.

    1983-01-01

    The authors survey the class of systolic special-purpose computer architectures and algorithms, which are particularly well-suited for implementation in very large scale integrated circuitry (VLSI). They give a brief introduction to systolic arrays for a reader with a broad technical background and some experience in using a computer, but who is not necessarily a computer scientist. In addition they briefly survey the technological advances in VLSI that led to the development of systolic algorithms and architectures. 38 references.

  2. 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.

  3. 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.

  4. 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.

  5. A novel PON based UMTS broadband wireless access network architecture with an algorithm to guarantee end to end QoS

    NASA Astrophysics Data System (ADS)

    Sana, Ajaz; Hussain, Shahab; Ali, Mohammed A.; Ahmed, Samir

    2007-09-01

    In this paper we proposes a novel Passive Optical Network (PON) based broadband wireless access network architecture to provide multimedia services (video telephony, video streaming, mobile TV, mobile emails etc) to mobile users. In the conventional wireless access networks, the base stations (Node B) and Radio Network Controllers (RNC) are connected by point to point T1/E1 lines (Iub interface). The T1/E1 lines are expensive and add up to operating costs. Also the resources (transceivers and T1/E1) are designed for peak hours traffic, so most of the time the dedicated resources are idle and wasted. Further more the T1/E1 lines are not capable of supporting bandwidth (BW) required by next generation wireless multimedia services proposed by High Speed Packet Access (HSPA, Rel.5) for Universal Mobile Telecommunications System (UMTS) and Evolution Data only (EV-DO) for Code Division Multiple Access 2000 (CDMA2000). The proposed PON based back haul can provide Giga bit data rates and Iub interface can be dynamically shared by Node Bs. The BW is dynamically allocated and the unused BW from lightly loaded Node Bs is assigned to heavily loaded Node Bs. We also propose a novel algorithm to provide end to end Quality of Service (QoS) (between RNC and user equipment).The algorithm provides QoS bounds in the wired domain as well as in wireless domain with compensation for wireless link errors. Because of the air interface there can be certain times when the user equipment (UE) is unable to communicate with Node B (usually referred to as link error). Since the link errors are bursty and location dependent. For a proposed approach, the scheduler at the Node B maps priorities and weights for QoS into wireless MAC. The compensations for errored links is provided by the swapping of services between the active users and the user data is divided into flows, with flows allowed to lag or lead. The algorithm guarantees (1)delay and throughput for error-free flows,(2)short term fairness

  6. 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).

  7. 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.

  8. Acoustic simulation in architecture with parallel algorithm

    NASA Astrophysics Data System (ADS)

    Li, Xiaohong; Zhang, Xinrong; Li, Dan

    2004-03-01

    In allusion to complexity of architecture environment and Real-time simulation of architecture acoustics, a parallel radiosity algorithm was developed. The distribution of sound energy in scene is solved with this method. And then the impulse response between sources and receivers at frequency segment, which are calculated with multi-process, are combined into whole frequency response. The numerical experiment shows that parallel arithmetic can improve the acoustic simulating efficiency of complex scene.

  9. Mapping algorithms on regular parallel architectures

    SciTech Connect

    Lee, P.

    1989-01-01

    It is significant that many of time-intensive scientific algorithms are formulated as nested loops, which are inherently regularly structured. In this dissertation the relations between the mathematical structure of nested loop algorithms and the architectural capabilities required for their parallel execution are studied. The architectural model considered in depth is that of an arbitrary dimensional systolic array. The mathematical structure of the algorithm is characterized by classifying its data-dependence vectors according to the new ZERO-ONE-INFINITE property introduced. Using this classification, the first complete set of necessary and sufficient conditions for correct transformation of a nested loop algorithm onto a given systolic array of an arbitrary dimension by means of linear mappings is derived. Practical methods to derive optimal or suboptimal systolic array implementations are also provided. The techniques developed are used constructively to develop families of implementations satisfying various optimization criteria and to design programmable arrays efficiently executing classes of algorithms. In addition, a Computer-Aided Design system running on SUN workstations has been implemented to help in the design. The methodology, which deals with general algorithms, is illustrated by synthesizing linear and planar systolic array algorithms for matrix multiplication, a reindexed Warshall-Floyd transitive closure algorithm, and the longest common subsequence algorithm.

  10. Convergence Analysis of a Cascade Architecture Neural Network

    NASA Technical Reports Server (NTRS)

    Duong, Tuan A.; Stubberub, Allen R.; Daud, Taher; Thakoor, Anil

    1997-01-01

    In this paper, we present a mathematical foundation, including a convergence analysis, for cascading architecture neural networks. From this, a mathematical foundation for the casade correlation learning algorithm can also be found. Furthermore, it becomes apparent that the cascade correlation scheme is a special case of an efficient hardware learning algorithm called Cascade Error Projection.

  11. 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

  12. 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.

  13. Array processor architecture connection network

    NASA Technical Reports Server (NTRS)

    Barnes, George H. (Inventor); Lundstrom, Stephen F. (Inventor); Shafer, Philip E. (Inventor)

    1982-01-01

    A connection network is disclosed for use between a parallel array of processors and a parallel array of memory modules for establishing non-conflicting data communications paths between requested memory modules and requesting processors. The connection network includes a plurality of switching elements interposed between the processor array and the memory modules array in an Omega networking architecture. Each switching element includes a first and a second processor side port, a first and a second memory module side port, and control logic circuitry for providing data connections between the first and second processor ports and the first and second memory module ports. The control logic circuitry includes strobe logic for examining data arriving at the first and the second processor ports to indicate when the data arriving is requesting data from a requesting processor to a requested memory module. Further, connection circuitry is associated with the strobe logic for examining requesting data arriving at the first and the second processor ports for providing a data connection therefrom to the first and the second memory module ports in response thereto when the data connection so provided does not conflict with a pre-established data connection currently in use.

  14. 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.

  15. Hybrid algorithm for NARX network parameters' determination using differential evolution and genetic algorithm

    NASA Astrophysics Data System (ADS)

    Salami, M. J. E.; Tijani, I. B.; Abdullateef, A. I.; Aibinu, M. A.

    2013-12-01

    A hybrid optimization algorithm using Differential Evolution (DE) and Genetic Algorithm (GA) is proposed in this study to address the problem of network parameters determination associated with the Nonlinear Autoregressive with eXogenous inputs Network (NARX-network). The proposed algorithm involves a two level optimization scheme to search for both optimal network architecture and weights. The DE at the upper level is formulated as combinatorial optimization to search for the network architecture while the associated network weights that minimize the prediction error is provided by the GA at the lower level. The performance of the algorithm is evaluated on identification of a laboratory rotary motion system. The system identification results show the effectiveness of the proposed algorithm for nonparametric model development.

  16. A Communications Network Architecture for Future Mars Missions

    NASA Astrophysics Data System (ADS)

    Gilstrap, R.; Alena, R.; Stone, T.

    2012-06-01

    We propose a Mars communications network architecture incorporating the Internet Protocol, small communications relay satellites, laser communications, delay tolerant networking, mobile ad hoc networking, and wireless sensor networks.

  17. Scalable Network Emulator Architecture for IP Optical Network Management

    NASA Astrophysics Data System (ADS)

    Oki, Eiji; Kitsuwan, Nattapong; Tsunoda, Shunichi; Miyamura, Takashi; Masuda, Akeo; Shiomoto, Kohei

    This letter proposes a scalable network emulator architecture to support IP optical network management. The network emulator uses the same router interfaces to communicate with the IP optical TE server as the actual IP optical network, and behaves as an actual IP optical network between the interfaces. The network emulator mainly consists of databases and three modules: interface module, resource simulator module, and traffic generator module. To make the network emulator scalable in terms of network size, we employ TCP/IP socket communications between the modules. The proposed network emulator has the benefit that its implementation is not strongly dependent on hardware limitations. We develop a prototype of the network emulator based on the proposed architecture. Our design and experiments show that the proposed architecture is effective.

  18. 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. PMID:26878722

  19. High-speed parallel-processing networks for advanced architectures

    SciTech Connect

    Morgan, D.R.

    1988-06-01

    This paper describes various parallel-processing architecture networks that are candidates for eventual airborne use. An attempt at projecting which type of network is suitable or optimum for specific metafunction or stand-alone applications is made. However, specific algorithms will need to be developed and bench marks executed before firm conclusions can be drawn. Also, a conceptual projection of how these processors can be built in small, flyable units through the use of wafer-scale integration is offered. The use of the PAVE PILLAR system architecture to provide system level support for these tightly coupled networks is described. The author concludes that: (1) extremely high processing speeds implemented in flyable hardware is possible through parallel-processing networks if development programs are pursued; (2) dramatic speed enhancements through parallel processing requires an excellent match between the algorithm and computer-network architecture; (3) matching several high speed parallel oriented algorithms across the aircraft system to a limited set of hardware modules may be the most cost-effective approach to achieving speed enhancements; and (4) software-development tools and improved operating systems will need to be developed to support efficient parallel-processor use.

  20. Advancements in metro optical network architectures

    NASA Astrophysics Data System (ADS)

    Paraschis, Loukas

    2005-02-01

    This paper discusses the innovation in network architectures, and optical transport, that enables metropolitan networks to cost-effectively scale to hundreds Gb/s of capacity, and to hundreds km of reach, and to also meet the diverse service needs of enterprise and residential applications. A converged metro network, where Ethernet/IP services, and traditional TDM traffic operate over an intelligent WDM transport layer is increasingly becoming the most attractive architecture addressing the primary need of network operators for significantly improved capital and operational network cost. At the same time, this converged network has to leverage advanced technology, and introduce intelligence in order to significantly improve the deployment and manageability of WDM transport. The most important system advancements and the associated technology innovations that enhance the cost-effectiveness of metropolitan optical networks are being reviewed.

  1. 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

  2. Systolic array architecture for convolutional decoding algorithms: Viterbi algorithm and stack algorithm

    SciTech Connect

    Chang, C.Y.

    1986-01-01

    New results on efficient forms of decoding convolutional codes based on Viterbi and stack algorithms using systolic array architecture are presented. Some theoretical aspects of systolic arrays are also investigated. First, systolic array implementation of Viterbi algorithm is considered, and various properties of convolutional codes are derived. A technique called strongly connected trellis decoding is introduced to increase the efficient utilization of all the systolic array processors. The issues dealing with the composite branch metric generation, survivor updating, overall system architecture, throughput rate, and computations overhead ratio are also investigated. Second, the existing stack algorithm is modified and restated in a more concise version so that it can be efficiently implemented by a special type of systolic array called systolic priority queue. Three general schemes of systolic priority queue based on random access memory, shift register, and ripple register are proposed. Finally, a systematic approach is presented to design systolic arrays for certain general classes of recursively formulated algorithms.

  3. 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.

  4. 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.

  5. An optimization methodology for neural network weights and architectures.

    PubMed

    Ludermir, Teresa B; Yamazaki, Akio; Zanchettin, Cleber

    2006-11-01

    This paper introduces a methodology for neural network global optimization. The aim is the simultaneous optimization of multilayer perceptron (MLP) network weights and architectures, in order to generate topologies with few connections and high classification performance for any data sets. The approach combines the advantages of simulated annealing, tabu search and the backpropagation training algorithm in order to generate an automatic process for producing networks with high classification performance and low complexity. Experimental results obtained with four classification problems and one prediction problem has shown to be better than those obtained by the most commonly used optimization techniques. PMID:17131660

  6. A security architecture for health information networks.

    PubMed

    Kailar, Rajashekar; Muralidhar, Vinod

    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

  7. 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

  8. 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

    2016-03-01

    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. PMID:26898468

  9. Mapping robust parallel multigrid algorithms to scalable memory architectures

    NASA Technical Reports Server (NTRS)

    Overman, Andrea; Vanrosendale, John

    1993-01-01

    The convergence rate of standard multigrid algorithms degenerates on problems with stretched grids or anisotropic operators. The usual cure for this is the use of line or plane relaxation. However, multigrid algorithms based on line and plane relaxation have limited and awkward parallelism and are quite difficult to map effectively to highly parallel architectures. Newer multigrid algorithms that overcome anisotropy through the use of multiple coarse grids rather than relaxation are better suited to massively parallel architectures because they require only simple point-relaxation smoothers. In this paper, we look at the parallel implementation of a V-cycle multiple semicoarsened grid (MSG) algorithm on distributed-memory architectures such as the Intel iPSC/860 and Paragon computers. The MSG algorithms provide two levels of parallelism: parallelism within the relaxation or interpolation on each grid and across the grids on each multigrid level. Both levels of parallelism must be exploited to map these algorithms effectively to parallel architectures. This paper describes a mapping of an MSG algorithm to distributed-memory architectures that demonstrates how both levels of parallelism can be exploited. The result is a robust and effective multigrid algorithm for distributed-memory machines.

  10. Mapping robust parallel multigrid algorithms to scalable memory architectures

    NASA Technical Reports Server (NTRS)

    Overman, Andrea; Vanrosendale, John

    1993-01-01

    The convergence rate of standard multigrid algorithms degenerates on problems with stretched grids or anisotropic operators. The usual cure for this is the use of line or plane relaxation. However, multigrid algorithms based on line and plane relaxation have limited and awkward parallelism and are quite difficult to map effectively to highly parallel architectures. Newer multigrid algorithms that overcome anisotropy through the use of multiple coarse grids rather than line relaxation are better suited to massively parallel architectures because they require only simple point-relaxation smoothers. The parallel implementation of a V-cycle multiple semi-coarsened grid (MSG) algorithm or distributed-memory architectures such as the Intel iPSC/860 and Paragon computers is addressed. The MSG algorithms provide two levels of parallelism: parallelism within the relaxation or interpolation on each grid and across the grids on each multigrid level. Both levels of parallelism must be exploited to map these algorithms effectively to parallel architectures. A mapping of an MSG algorithm to distributed-memory architectures that demonstrate how both levels of parallelism can be exploited is described. The results is a robust and effective multigrid algorithm for distributed-memory machines.

  11. 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

  12. Algorithmically Specialized Parallel Architecture For Robotics

    NASA Technical Reports Server (NTRS)

    Fijany, Amir; Bejczy, Antal K.

    1991-01-01

    Computing system called Robot Mathematics Processor (RMP) contains large number of processor elements (PE's) connected in various parallel and serial combinations reconfigurable via software. Special-purpose architecture designed for solving diverse computational problems in robot control, simulation, trajectory generation, workspace analysis, and like. System an MIMD-SIMD parallel architecture capable of exploiting parallelism in different forms and at several computational levels. Major advantage lies in design of cells, which provides flexibility and reconfigurability superior to previous SIMD processors.

  13. 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

  14. Heterogeneous waveband switching in wavelength division multiplexed networks based on autonomous clustering architecture [Invited

    NASA Astrophysics Data System (ADS)

    Li, Mengke; Ramamurthy, Byrav

    2006-09-01

    Adopting waveband switching (WBS) in backbone wavelength division multiplexed (WDM) networks is promising since it can reduce the network operational cost and the call blocking probability. However, upgrading the existing optical switching architecture requires time and money. Thus heterogeneous waveband switching (HeteroWBS) architecture would be desirable in such a system, where some nodes can support WBS functions and some cannot. We study the performance of HeteroWBS networks in terms of call blocking probability and cost savings under dynamically arriving traffic requests. We first investigate the effects of optical component developments on waveband switching in WDM networks. Various connection managements are then listed and analyzed. Next, to assist in the designing of efficient WBS algorithms, an autonomous clustering-based HeteroWBS (AS-HeteroWBS) architecture is proposed. The AS-HeteroWBS architecture clusters the network into multiple autonomous systems (ASs). An AS may contain some specific nodes that provide WBS functions for all the nodes in the AS. Based on the architecture, three HeteroWBS algorithms are proposed, namely, the autonomous heterogeneous WBS algorithm (AS-WBS), the autonomous source-limited heterogeneous WBS algorithm (AS-S-WBS), and the shortest-path-based heterogeneous WBS algorithm (SH-WBS). Our simulation results show that the HeteroWBS algorithms can achieve optimal cost savings while maintaining the same network throughput compared with the algorithm without WBS.

  15. Improving neural network performance on SIMD architectures

    NASA Astrophysics Data System (ADS)

    Limonova, Elena; Ilin, Dmitry; Nikolaev, Dmitry

    2015-12-01

    Neural network calculations for the image recognition problems can be very time consuming. In this paper we propose three methods of increasing neural network performance on SIMD architectures. The usage of SIMD extensions is a way to speed up neural network processing available for a number of modern CPUs. In our experiments, we use ARM NEON as SIMD architecture example. The first method deals with half float data type for matrix computations. The second method describes fixed-point data type for the same purpose. The third method considers vectorized activation functions implementation. For each method we set up a series of experiments for convolutional and fully connected networks designed for image recognition task.

  16. 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.

  17. 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.

  18. A Layered Approach To Pacs Network Architecture

    NASA Astrophysics Data System (ADS)

    Hegde, Shankar S.; Prewitt, Judith M.

    1984-08-01

    Although the functions performed by the different nodes on the PACS network are many, it is possible to formulate a minimum set of service primitives such that the application software residing at the nodes can utilize those primitives to perform the functions. These primitives define the framework for the communication interface. The question of how these primitives fit into the concept of a layered network architecture is explored in this paper. The OSI model as applicable to the PACS network is described, the areas that need standardization are briefly mentioned, and the ongoing standardization efforts are addressed from the OSI perspective.

  19. 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.

  20. Consensus algorithms in decentralized networks

    NASA Astrophysics Data System (ADS)

    Coduti, Leonardo Phillip

    We consider a decentralized network with the following goal: the state at each node of the network iteratively converges to the same value. Ensuring that this goal is achieved requires certain properties of the topology of the network and the function describing the evolution of the network. We will present these properties for deterministic systems, extending current results in the literature. As an additional contribution, we will show how the convergence results for stochastic systems are direct consequences of the corresponding deterministic systems, drastically simplifying many other current results. In general, these consensus systems can be both time invariant and time varying, and we will extend all our deterministic and stochastic results to include time varying systems as well. We will then consider a more complex consensus problem, the resource allocation problem. In this situation each node of the network has both a state and a capacity. The capacity is a monotone increasing function of the state, and the goal is for the nodes to exchange capacity in a decentralized manner in order to drive all of the states to the same value. Conditions ensuring consensus in the deterministic setting will be presented, and we will show how convergence in this system also comes from the fundamental deterministic result for consensus algorithms. The main results will again be extended to stochastic and time varying systems. The linear consensus system requires the construction of a matrix of weighting parameters with specific properties. We present an iterative algorithm for determining the weighting parameters in a decentralized fashion; the weighting parameters are specified by the nodes and each node only specifies the weighting parameters as sociated with that node. The results assume that the communication graph of the network is directed, and we consider both synchronous communication, and stochastic asynchronous networks.

  1. The architecture of the climate network

    NASA Astrophysics Data System (ADS)

    Tsonis, A. A.; Roebber, P. J.

    2004-02-01

    We consider climate as a network of many dynamical systems and apply ideas from graph theory to a global data set to study its collective behavior. We find that the network has properties of ‘small-world’ networks (Nature 393 (1999) 440). A detailed investigation of the coupling architecture of this network reveals that the overall dynamics emerge from the interaction of two interweaved subnetworks. One subnetwork operates in the tropics and the other at higher latitudes with the equatorial one acting as an agent that establishes links between the two hemispheres. Both subsystems are ‘small-world’ networks, but there are distinct differences between the two subsystems. The tropical one is an almost fully connected network, whereas the mid-latitude one is more like a scale-free network characterized by dominant super nodes, and multifractal properties. This unique architecture may lead to new insights not only about the dynamics of the climate system but of other spatially extended complex systems with a large number of degrees of freedom.

  2. 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.

  3. Architectures of fiber optic network in telecommunications

    NASA Astrophysics Data System (ADS)

    Vasile, Irina B.; Vasile, Alexandru; Filip, Luminita E.

    2005-08-01

    The operators of telecommunications have targeted their efforts towards realizing applications using broad band fiber optics systems in the access network. Thus, a new concept related to the implementation of fiber optic transmission systems, named FITL (Fiber In The Loop) has appeared. The fiber optic transmission systems have been extensively used for realizing the transport and intercommunication of the public telecommunication network, as well as for assuring the access to the telecommunication systems of the great corporations. Still, the segment of the residential users and small corporations did not benefit on large scale of this technology implementation. For the purpose of defining fiber optic applications, more types of architectures were conceived, like: bus, ring, star, tree. In the case of tree-like networks passive splitters (that"s where the name of PON comes from - Passive Optical Network-), which reduce significantly the costs of the fiber optic access, by separating the costs of the optical electronic components. That's why the passive fiber optics architectures (PON represent a viable solution for realizing the access at the user's loop. The main types of fiber optics architectures included in this work are: FTTC (Fiber To The Curb); FTTB (Fiber To The Building); FTTH (Fiber To The Home).

  4. A vehicle detection algorithm based on deep belief network.

    PubMed

    Wang, Hai; Cai, Yingfeng; Chen, Long

    2014-01-01

    Vision based vehicle detection is a critical technology that plays an important role in not only vehicle active safety but also road video surveillance application. Traditional shallow model based vehicle detection algorithm still cannot meet the requirement of accurate vehicle detection in these applications. In this work, a novel deep learning based vehicle detection algorithm with 2D deep belief network (2D-DBN) is proposed. In the algorithm, the proposed 2D-DBN architecture uses second-order planes instead of first-order vector as input and uses bilinear projection for retaining discriminative information so as to determine the size of the deep architecture which enhances the success rate of vehicle detection. On-road experimental results demonstrate that the algorithm performs better than state-of-the-art vehicle detection algorithm in testing data sets. PMID:24959617

  5. 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.

  6. 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. PMID:24126252

  7. 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.

  8. 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.

  9. Constructive neural-network learning algorithms for pattern classification.

    PubMed

    Parekh, R; Yang, J; Honavar, V

    2000-01-01

    Constructive learning algorithms offer an attractive approach for the incremental construction of near-minimal neural-network architectures for pattern classification. They help overcome the need for ad hoc and often inappropriate choices of network topology in algorithms that search for suitable weights in a priori fixed network architectures. Several such algorithms are proposed in the literature and shown to converge to zero classification errors (under certain assumptions) on tasks that involve learning a binary to binary mapping (i.e., classification problems involving binary-valued input attributes and two output categories). We present two constructive learning algorithms MPyramid-real and MTiling-real that extend the pyramid and tiling algorithms, respectively, for learning real to M-ary mappings (i.e., classification problems involving real-valued input attributes and multiple output classes). We prove the convergence of these algorithms and empirically demonstrate their applicability to practical pattern classification problems. Additionally, we show how the incorporation of a local pruning step can eliminate several redundant neurons from MTiling-real networks. PMID:18249773

  10. Re-engineering Nascom's network management architecture

    NASA Astrophysics Data System (ADS)

    Drake, Brian C.; Messent, David

    1994-11-01

    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

  11. 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

  12. 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.

  13. 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.

  14. The architecture of complex weighted networks

    PubMed Central

    Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A.

    2004-01-01

    Networked structures arise in a wide array of different contexts such as technological and transportation infrastructures, social phenomena, and biological systems. These highly interconnected systems have recently been the focus of a great deal of attention that has uncovered and characterized their topological complexity. Along with a complex topological structure, real networks display a large heterogeneity in the capacity and intensity of the connections. These features, however, have mainly not been considered in past studies where links are usually represented as binary states, i.e., either present or absent. Here, we study the scientific collaboration network and the world-wide air-transportation network, which are representative examples of social and large infrastructure systems, respectively. In both cases it is possible to assign to each edge of the graph a weight proportional to the intensity or capacity of the connections among the various elements of the network. We define appropriate metrics combining weighted and topological observables that enable us to characterize the complex statistical properties and heterogeneity of the actual strength of edges and vertices. This information allows us to investigate the correlations among weighted quantities and the underlying topological structure of the network. These results provide a better description of the hierarchies and organizational principles at the basis of the architecture of weighted networks. PMID:15007165

  15. 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.

  16. 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.

  17. Development of the Brain's Functional Network Architecture

    PubMed Central

    Power, Jonathan D.; Petersen, Steven E.; Schlaggar, Bradley L.

    2013-01-01

    A full understanding of the development of the brain's functional network architecture requires not only an understanding of developmental changes in neural processing in individual brain regions but also an understanding of changes in inter-regional interactions. Resting state functional connectivity MRI (rs-fcMRI) is increasingly being used to study functional interactions between brain regions in both adults and children. We briefly review methods used to study functional interactions and networks with rs-fcMRI and how these methods have been used to define developmental changes in network functional connectivity. The developmental rs-fcMRI studies to date have found two general properties. First, regional interactions change from being predominately anatomically local in children to interactions spanning longer cortical distances in young adults. Second, this developmental change in functional connectivity occurs, in general, via mechanisms of segregation of local regions and integration of distant regions into disparate subnetworks. PMID:20976563

  18. 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.

  19. 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.

  20. A TLD dose algorithm using artificial neural networks

    SciTech Connect

    Moscovitch, M.; Rotunda, J.E.; Tawil, R.A.; Rathbone, B.A.

    1995-12-31

    An artificial neural network was designed and used to develop a dose algorithm for a multi-element thermoluminescence dosimeter (TLD). The neural network architecture is based on the concept of functional links network (FLN). Neural network is an information processing method inspired by the biological nervous system. A dose algorithm based on neural networks is fundamentally different as compared to conventional algorithms, as it has the capability to learn from its own experience. The neural network algorithm is shown the expected dose values (output) associated with given responses of a multi-element dosimeter (input) many times. The algorithm, being trained that way, eventually is capable to produce its own unique solution to similar (but not exactly the same) dose calculation problems. For personal dosimetry, the output consists of the desired dose components: deep dose, shallow dose and eye dose. The input consists of the TL data obtained from the readout of a multi-element dosimeter. The neural network approach was applied to the Harshaw Type 8825 TLD, and was shown to significantly improve the performance of this dosimeter, well within the U.S. accreditation requirements for personnel dosimeters.

  1. 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.

  2. Network architecture for global biomedical monitoring service.

    PubMed

    Lopez-Casado, Carmen; Tejero-Calado, Juan; Bernal-Martin, Antonio; Lopez-Gomez, Miguel; Romero-Romero, Marco; Quesada, Guillermo; Lorca, Julio; Garcia, Eugenia

    2005-01-01

    Most of the patients who are in hospitals and, increasingly, patients controlled remotely from their homes, at-home monitoring, are continuously monitored in order to control their evolution. The medical devices used up to now, force the sanitary staff to go to the patients' room to control the biosignals that are being monitored, although in many cases, patients are in perfect conditions. If patient is at home, it is he or she who has to go to the hospital to take the record of the monitored signal. New wireless technologies, such as BlueTooth and WLAN, make possible the deployment of systems that allow the display and storage of those signals in any place where the hospital intranet is accessible. In that way, unnecessary displacements are avoided. This paper presents a network architecture that allows the identification of the biosignal acquisition device as IP network nodes. The system is based on a TCP/IP architecture which is scalable and avoids the deployment of a specific purpose network. PMID:17282729

  3. 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.

  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. A Biomimetic Adaptive Algorithm and Low-Power Architecture for Implantable Neural Decoders

    PubMed Central

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

    2010-01-01

    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. PMID:19964345

  7. Quadrant architecture for fast in-place algorithms

    SciTech Connect

    Besslich, P.W.; Kurowski, J.O.

    1983-10-01

    The architecture proposed is tailored to support Radix-2/sup k/ based in-place processing of pictorial data. The algorithms make use of signal-flow graphs to describe 2-dimensional in-place operations suitable for image processing. They may be executed on a general-purpose computer but may also be supported by a special parallel architecture. Major advantages of the scheme are in-place processing and parallel access to disjoint sections of memory only. A quadtree-like decomposition of the picture prevents blocking and queuing of private and common buses. 9 references.

  8. 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.

  9. 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.

  10. Network-centric MFA tracking architecture based on soft-level data association

    NASA Astrophysics Data System (ADS)

    Novoselov, Roman; Gadaleta, Sabino; Poore, Aubrey

    2005-09-01

    This work presents a new network centric architecture for multiple frame assignment (MFA) tracking. The architecture improves on earlier network tracking schemes by allowing trackers to broadcast decisions about their local soft-level associations, via the Soft Associated Measurement Reports (SAMRs). The SAMR may be followed by an "Oops" message, if the soft association was incorrect and must be revoked. We show, however, that such revocations are very rare in most scenarios. This paper discusses the implementation of the new algorithm and presents simulation results. Considerable improvements in the consistency of the air picture are demonstrated, owing to the the reduced latency in transmission of measurement-to-track associations. The earlier network architectures, namely, the Centralized MFA, the Replicated Centralized MFA, and the Network MFA on Local and All Data, are also discussed in this work, as they form the foundation for the "Oops" algorithm.

  11. 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.

  12. 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

  13. 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,…

  14. A Dynamic Navigation Algorithm Considering Network Disruptions

    NASA Astrophysics Data System (ADS)

    Jiang, J.; Wu, L.

    2014-04-01

    In traffic network, link disruptions or recoveries caused by sudden accidents, bad weather and traffic congestion, lead to significant increase or decrease in travel times on some network links. Similar situation also occurs in real-time emergency evacuation plan in indoor areas. As the dynamic nature of real-time network information generates better navigation solutions than the static one, a real-time dynamic navigation algorithm for emergency evacuation with stochastic disruptions or recoveries in the network is presented in this paper. Compared with traditional existing algorithms, this new algorithm adjusts pre-existing path to a new optimal one according to the changing link travel time. With real-time network information, it can provide the optional path quickly to adapt to the rapid changing network properties. Theoretical analysis and experimental results demonstrate that this proposed algorithm performs a high time efficiency to get exact solution and indirect information can be calculated in spare time.

  15. On-line learning algorithms for locally recurrent neural networks.

    PubMed

    Campolucci, P; Uncini, A; Piazza, F; Rao, B D

    1999-01-01

    This paper focuses on on-line learning procedures for locally recurrent neural networks with emphasis on multilayer perceptron (MLP) with infinite impulse response (IIR) synapses and its variations which include generalized output and activation feedback multilayer networks (MLN's). We propose a new gradient-based procedure called recursive backpropagation (RBP) whose on-line version, causal recursive backpropagation (CRBP), presents some advantages with respect to the other on-line training methods. The new CRBP algorithm includes as particular cases backpropagation (BP), temporal backpropagation (TBP), backpropagation for sequences (BPS), Back-Tsoi algorithm among others, thereby providing a unifying view on gradient calculation techniques for recurrent networks with local feedback. The only learning method that has been proposed for locally recurrent networks with no architectural restriction is the one by Back and Tsoi. The proposed algorithm has better stability and higher speed of convergence with respect to the Back-Tsoi algorithm, which is supported by the theoretical development and confirmed by simulations. The computational complexity of the CRBP is comparable with that of the Back-Tsoi algorithm, e.g., less that a factor of 1.5 for usual architectures and parameter settings. The superior performance of the new algorithm, however, easily justifies this small increase in computational burden. In addition, the general paradigms of truncated BPTT and RTRL are applied to networks with local feedback and compared with the new CRBP method. The simulations show that CRBP exhibits similar performances and the detailed analysis of complexity reveals that CRBP is much simpler and easier to implement, e.g., CRBP is local in space and in time while RTRL is not local in space. PMID:18252525

  16. 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

  17. A Cluster-Based Architecture to Structure the Topology of Parallel Wireless Sensor Networks

    PubMed Central

    Lloret, Jaime; Garcia, Miguel; Bri, Diana; Diaz, Juan R.

    2009-01-01

    A wireless sensor network is a self-configuring network of mobile nodes connected by wireless links where the nodes have limited capacity and energy. In many cases, the application environment requires the design of an exclusive network topology for a particular case. Cluster-based network developments and proposals in existence have been designed to build a network for just one type of node, where all nodes can communicate with any other nodes in their coverage area. Let us suppose a set of clusters of sensor nodes where each cluster is formed by different types of nodes (e.g., they could be classified by the sensed parameter using different transmitting interfaces, by the node profile or by the type of device: laptops, PDAs, sensor etc.) and exclusive networks, as virtual networks, are needed with the same type of sensed data, or the same type of devices, or even the same type of profiles. In this paper, we propose an algorithm that is able to structure the topology of different wireless sensor networks to coexist in the same environment. It allows control and management of the topology of each network. The architecture operation and the protocol messages will be described. Measurements from a real test-bench will show that the designed protocol has low bandwidth consumption and also demonstrates the viability and the scalability of the proposed architecture. Our ccluster-based algorithm is compared with other algorithms reported in the literature in terms of architecture and protocol measurements. PMID:22303185

  18. A flexible algorithm for calculating pair interactions on SIMD architectures

    NASA Astrophysics Data System (ADS)

    Páll, Szilárd; Hess, Berk

    2013-12-01

    Calculating interactions or correlations between pairs of particles is typically the most time-consuming task in particle simulation or correlation analysis. Straightforward implementations using a double loop over particle pairs have traditionally worked well, especially since compilers usually do a good job of unrolling the inner loop. In order to reach high performance on modern CPU and accelerator architectures, single-instruction multiple-data (SIMD) parallelization has become essential. Avoiding memory bottlenecks is also increasingly important and requires reducing the ratio of memory to arithmetic operations. Moreover, when pairs only interact within a certain cut-off distance, good SIMD utilization can only be achieved by reordering input and output data, which quickly becomes a limiting factor. Here we present an algorithm for SIMD parallelization based on grouping a fixed number of particles, e.g. 2, 4, or 8, into spatial clusters. Calculating all interactions between particles in a pair of such clusters improves data reuse compared to the traditional scheme and results in a more efficient SIMD parallelization. Adjusting the cluster size allows the algorithm to map to SIMD units of various widths. This flexibility not only enables fast and efficient implementation on current CPUs and accelerator architectures like GPUs or Intel MIC, but it also makes the algorithm future-proof. We present the algorithm with an application to molecular dynamics simulations, where we can also make use of the effective buffering the method introduces.

  19. 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.

  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. MediaMesh: an architecture for integrating isochronous processing algorithms into media servers

    NASA Astrophysics Data System (ADS)

    Amini, Lisa D.; Lepre, Jorge; Kienzle, Martin G.

    1999-12-01

    Current media servers do not provide the generality required to easily integrate arbitrary isochronous processing algorithms into streams of continuous media. Specifically, present day video server architectures primarily focus on disk and network strategies for efficiently managing available resources under stringent QoS guarantees. However, they do not fully consider the problems of integrating the wide variety of algorithms required for interactive multimedia applications. Examples of applications benefiting from a more flexible server environment include watermarking, encrypting or scrambling streams, visual VCR operations, and multiplexing or demultiplexing of live presentations. In this paper, we detail the MediaMesh architecture for integrating arbitrary isochronous processing algorithms into general purpose media servers. Our framework features a programming model through which user-written modules can be dynamically loaded and interconnected in self-managing graphs of stream processing components. Design highlights include novel techniques for distributed stream control, efficient buffer management and QoS management. To demonstrate its applicability, we have implemented the MediaMesh architecture in the context of a commercial video server. We illustrate the viability of the architecture through performance data collected from four processing modules that were implemented to facilitate new classes of applications on our video server.

  2. 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.

  3. From Point Clouds to Architectural Models: Algorithms for Shape Reconstruction

    NASA Astrophysics Data System (ADS)

    Canciani, M.; Falcolini, C.; Saccone, M.; Spadafora, G.

    2013-02-01

    The use of terrestrial laser scanners in architectural survey applications has become more and more common. Row data complexity, as given by scanner restitution, leads to several problems about design and 3D-modelling starting from Point Clouds. In this context we present a study on architectural sections and mathematical algorithms for their shape reconstruction, according to known or definite geometrical rules, focusing on shapes of different complexity. Each step of the semi-automatic algorithm has been developed using Mathematica software and CAD, integrating both programs in order to reconstruct a geometrical CAD model of the object. Our study is motivated by the fact that, for architectural survey, most of three dimensional modelling procedures concerning point clouds produce superabundant, but often unnecessary, information and are also very expensive in terms of cpu time using more and more sophisticated hardware and software. On the contrary, it's important to simplify/decimate the point cloud in order to recognize a particular form out of some definite geometric/architectonic shapes. Such a process consists of several steps: first the definition of plane sections and characterization of their architecture; secondly the construction of a continuous plane curve depending on some parameters. In the third step we allow the selection on the curve of some nodal points with given specific characteristics (symmetry, tangency conditions, shadowing exclusion, corners, … ). The fourth and last step is the construction of a best shape defined by the comparison with an abacus of known geometrical elements, such as moulding profiles, leading to a precise architectonical section. The algorithms have been developed and tested in very different situations and are presented in a case study of complex geometries such as some mouldings profiles in the Church of San Carlo alle Quattro Fontane.

  4. A generalized LSTM-like training algorithm for second-order recurrent neural networks

    PubMed Central

    Monner, Derek; Reggia, James A.

    2011-01-01

    The Long Short Term Memory (LSTM) is a second-order recurrent neural network architecture that excels at storing sequential short-term memories and retrieving them many time-steps later. LSTM’s original training algorithm provides the important properties of spatial and temporal locality, which are missing from other training approaches, at the cost of limiting it’s applicability to a small set of network architectures. Here we introduce the Generalized Long Short-Term Memory (LSTM-g) training algorithm, which provides LSTM-like locality while being applicable without modification to a much wider range of second-order network architectures. With LSTM-g, all units have an identical set of operating instructions for both activation and learning, subject only to the configuration of their local environment in the network; this is in contrast to the original LSTM training algorithm, where each type of unit has its own activation and training instructions. When applied to LSTM architectures with peephole connections, LSTM-g takes advantage of an additional source of back-propagated error which can enable better performance than the original algorithm. Enabled by the broad architectural applicability of LSTM-g, we demonstrate that training recurrent networks engineered for specific tasks can produce better results than single-layer networks. We conclude that LSTM-g has the potential to both improve the performance and broaden the applicability of spatially and temporally local gradient-based training algorithms for recurrent neural networks. PMID:21803542

  5. 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…

  6. 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

  7. Parameter incremental learning algorithm for neural networks.

    PubMed

    Wan, Sheng; Banta, Larry E

    2006-11-01

    In this paper, a novel stochastic (or online) training algorithm for neural networks, named parameter incremental learning (PIL) algorithm, is proposed and developed. The main idea of the PIL strategy is that the learning algorithm should not only adapt to the newly presented input-output training pattern by adjusting parameters, but also preserve the prior results. A general PIL algorithm for feedforward neural networks is accordingly presented as the first-order approximate solution to an optimization problem, where the performance index is the combination of proper measures of preservation and adaptation. The PIL algorithms for the multilayer perceptron (MLP) are subsequently derived. Numerical studies show that for all the three benchmark problems used in this paper the PIL algorithm for MLP is measurably superior to the standard online backpropagation (BP) algorithm and the stochastic diagonal Levenberg-Marquardt (SDLM) algorithm in terms of the convergence speed and accuracy. Other appealing features of the PIL algorithm are that it is computationally as simple as the BP algorithm, and as easy to use as the BP algorithm. It, therefore, can be applied, with better performance, to any situations where the standard online BP algorithm is applicable. PMID:17131658

  8. Efficient algorithms for survivable virtual network embedding

    NASA Astrophysics Data System (ADS)

    Sun, Gang; Yu, Hongfang; Li, Lemin; Anand, Vishal; di, Hao; Gao, Xiujiao

    2010-12-01

    Network Virtualization Technology is serving as an effective method for providing a flexible and highly adaptable shared substrate network to satisfy the diversity of demands. But the problem of efficiently embedding Virtual Network (VN) onto substrate network is intractable since it is NP-hard. How to guarantee survivability of the embedding efficiently is another great challenge. In this paper, we investigate the Survivable Virtual Network Embedding (SVNE) problem and propose two efficient algorithms for solving this problem efficiently. Firstly, we formulate the model with minimum-cost objective of survivable network virtualization problem by Mixed Integer Linear Programming (MILP). We then devise two efficient relaxation-based algorithms for solving survivable virtual network embedding problem: (1) Lagrangian Relaxation based algorithm, called LR-SVNE in this paper; (2) Decomposition based algorithm called DSVNE in this paper. The results of simulation experiments show that these two algorithms both have good performance on time efficiency but LR-SVNE can guarantee the solution converge to optimal one under small scale substrate network.

  9. 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. PMID:22181467

  10. A More Efficient COPE Architecture for Network Coding in Multihop Wireless Networks

    NASA Astrophysics Data System (ADS)

    Chi, Kaikai; Jiang, Xiaohong; Horiguchi, Susumu

    Recently, a promising packet forwarding architecture COPE was proposed to essentially improve the throughput of multihop wireless networks, where each network node can intelligently encode multiple packets together and forward them in a single transmission. However, COPE is still in its infancy and has the following limitations: (1) COPE adopts the FIFO packet scheduling and thus does not provide different priorities for different types of packets. (2) COPE simply classifies all packets destined to the same nexthop into small-size or large-size virtual queues and examines only the head packet of each virtual queue to find coding solutions. Such a queueing structure will lose some potential coding opportunities, because among packets destined to the same nexthop at most two packets (the head packets of small-size and large-size queues) will be examined in the coding process, regardless of the number of flows. (3) The coding algorithm adopted in COPE is fast but cannot always find good solutions. In order to address the above limitations, in this paper we first present a new queueing structure for COPE, which can provide more potential coding opportunities, and then propose a new packet scheduling algorithm for this queueing structure to assign different priorities to different types of packets. Finally, we propose an efficient coding algorithm to find appropriate packets for coding. Simulation results demonstrate that this new COPE architecture can further greatly improve the node transmission efficiency.

  11. Spatial search algorithms on Hanoi networks

    NASA Astrophysics Data System (ADS)

    Marquezino, Franklin de Lima; Portugal, Renato; Boettcher, Stefan

    2013-01-01

    We use the abstract search algorithm and its extension due to Tulsi to analyze a spatial quantum search algorithm that finds a marked vertex in Hanoi networks of degree 4 faster than classical algorithms. We also analyze the effect of using non-Groverian coins that take advantage of the small-world structure of the Hanoi networks. We obtain the scaling of the total cost of the algorithm as a function of the number of vertices. We show that Tulsi's technique plays an important role to speed up the searching algorithm. We can improve the algorithm's efficiency by choosing a non-Groverian coin if we do not implement Tulsi's method. Our conclusions are based on numerical implementations.

  12. 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.

  13. 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.

  14. A digital architecture for support vector machines: theory, algorithm, and FPGA implementation.

    PubMed

    Anguita, D; Boni, A; Ridella, S

    2003-01-01

    In this paper, we propose a digital architecture for support vector machine (SVM) learning and discuss its implementation on a field programmable gate array (FPGA). We analyze briefly the quantization effects on the performance of the SVM in classification problems to show its robustness, in the feedforward phase, respect to fixed-point math implementations; then, we address the problem of SVM learning. The architecture described here makes use of a new algorithm for SVM learning which is less sensitive to quantization errors respect to the solution appeared so far in the literature. The algorithm is composed of two parts: the first one exploits a recurrent network for finding the parameters of the SVM; the second one uses a bisection process for computing the threshold. The architecture implementing the algorithm is described in detail and mapped on a real current-generation FPGA (Xilinx Virtex II). Its effectiveness is then tested on a channel equalization problem, where real-time performances are of paramount importance. PMID:18244555

  15. 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.

  16. 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.

  17. 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. PMID:22970876

  18. Geometric algorithms for sensor networks.

    PubMed

    Gao, Jie; Guibas, Leonidas

    2012-01-13

    This paper surveys the use of geometric methods for wireless sensor networks. The close relationship of sensor nodes with their embedded physical space imposes a unique geometric character on such systems. The physical locations of the sensor nodes greatly impact on system design in all aspects, from low-level networking and organization to high-level information processing and applications. This paper reviews work in the past 10 years on topics such as network localization, geometric routing, information discovery, data-centric routing and topology discovery. PMID:22124080

  19. SCORPIUS algorithm benchmarks on the image understanding architecture machine

    NASA Astrophysics Data System (ADS)

    Bogdanowicz, Julius F.; Nash, J. Gregory; Shu, David B.

    1992-04-01

    Many Hughes tactical and strategic programs need high performance image processing. For example, photo-interpretation applications can require up to four orders of magnitude speedup over conventional computer architectures. Therefore, parallel processing systems are needed to help close the processing gap. Vision applications can usually be decomposed into three levels of processing called high, intermediate, and low level vision. Each processing level typically requires different types of numeric/symbolic computation, processing task granularities, and communications bandwidths. No parallel processing system is commercially available that is optimized for the entire range of computations. To meet these processing challenges, the image understanding architecture (IUA) has been developed by Hughes in collaboration with the University of Massachusetts. The IUA is a heterogeneous, hierarchical, associative parallel processor that is organized in three levels corresponding to the vision problem. Its lowest level consists of a large content addressable array parallel processor. This array of 'per pixel' bit serial processors is used for fixed point, low level numeric, and symbolic computations. The middle level is an interface communications array processor (ICAP). ICAP is an array of digital signal processing chips from TI TMS320Cx line, used for high speed number crunching. The highest level is the symbolic processing array. It is an array of general purpose microprocessors in which the artificial intelligence content of the image understanding software resides. A set of benchmarks from the DARPA/ORD sponsored SCORPIUS program were developed using the IUA. The set of algorithms included low level image processing as well as high level matching algorithms. Benchmark performance on the second generation IUA hardware is over four orders of magnitude faster than equivalent algorithms implemented on a DEC VAX 8650. The first generation hardware is operational. Development

  20. 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…

  1. Hierarchical link clustering algorithm in networks

    NASA Astrophysics Data System (ADS)

    Bodlaj, Jernej; Batagelj, Vladimir

    2015-06-01

    Hierarchical network clustering is an approach to find tightly and internally connected clusters (groups or communities) of nodes in a network based on its structure. Instead of nodes, it is possible to cluster links of the network. The sets of nodes belonging to clusters of links can overlap. While overlapping clusters of nodes are not always expected, they are natural in many applications. Using appropriate dissimilarity measures, we can complement the clustering strategy to consider, for example, the semantic meaning of links or nodes based on their properties. We propose a new hierarchical link clustering algorithm which in comparison to existing algorithms considers node and/or link properties (descriptions, attributes) of the input network alongside its structure using monotonic dissimilarity measures. The algorithm determines communities that form connected subnetworks (relational constraint) containing locally similar nodes with respect to their description. It is only implicitly based on the corresponding line graph of the input network, thus reducing its space and time complexities. We investigate both complexities analytically and statistically. Using provided dissimilarity measures, our algorithm can, in addition to the general overlapping community structure of input networks, uncover also related subregions inside these communities in a form of hierarchy. We demonstrate this ability on real-world and artificial network examples.

  2. Hierarchical link clustering algorithm in networks.

    PubMed

    Bodlaj, Jernej; Batagelj, Vladimir

    2015-06-01

    Hierarchical network clustering is an approach to find tightly and internally connected clusters (groups or communities) of nodes in a network based on its structure. Instead of nodes, it is possible to cluster links of the network. The sets of nodes belonging to clusters of links can overlap. While overlapping clusters of nodes are not always expected, they are natural in many applications. Using appropriate dissimilarity measures, we can complement the clustering strategy to consider, for example, the semantic meaning of links or nodes based on their properties. We propose a new hierarchical link clustering algorithm which in comparison to existing algorithms considers node and/or link properties (descriptions, attributes) of the input network alongside its structure using monotonic dissimilarity measures. The algorithm determines communities that form connected subnetworks (relational constraint) containing locally similar nodes with respect to their description. It is only implicitly based on the corresponding line graph of the input network, thus reducing its space and time complexities. We investigate both complexities analytically and statistically. Using provided dissimilarity measures, our algorithm can, in addition to the general overlapping community structure of input networks, uncover also related subregions inside these communities in a form of hierarchy. We demonstrate this ability on real-world and artificial network examples. PMID:26172761

  3. 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

  4. Predicate calculus for an architecture of multiple neural networks

    NASA Astrophysics Data System (ADS)

    Consoli, Robert H.

    1990-08-01

    Future projects with neural networks will require multiple individual network components. Current efforts along these lines are ad hoc. This paper relates the neural network to a classical device and derives a multi-part architecture from that model. Further it provides a Predicate Calculus variant for describing the location and nature of the trainings and suggests Resolution Refutation as a method for determining the performance of the system as well as the location of needed trainings for specific proofs. 2. THE NEURAL NETWORK AND A CLASSICAL DEVICE Recently investigators have been making reports about architectures of multiple neural networksL234. These efforts are appearing at an early stage in neural network investigations they are characterized by architectures suggested directly by the problem space. Touretzky and Hinton suggest an architecture for processing logical statements1 the design of this architecture arises from the syntax of a restricted class of logical expressions and exhibits syntactic limitations. In similar fashion a multiple neural netword arises out of a control problem2 from the sequence learning problem3 and from the domain of machine learning. 4 But a general theory of multiple neural devices is missing. More general attempts to relate single or multiple neural networks to classical computing devices are not common although an attempt is made to relate single neural devices to a Turing machines and Sun et a!. develop a multiple neural architecture that performs pattern classification.

  5. A modular architecture for wireless sensor network nodes

    NASA Astrophysics Data System (ADS)

    Davis, Jesse; Berry, Nina

    2004-09-01

    The system level hardware architecture of individual nodes in a wireless distributed sensor network has not received adequate attention. A large portion of the development work in wireless sensor networks has been devoted to the networking layer or the network communications, but considering the tight integration required between the hardware and software on each node can result in major benefits in power, performance, and usability as well. A novel hardware architecture based on the concept of task specific modular computing provides both the high flexibility and power efficiency required for effective distributed sensing solutions. A comparative power analysis with a traditional, centralized architecture gives a justifying motivation for pursuing the modular architecture. Finally, three decentralized module self-control mechanisms developed to minimize total system power will be presented and explained in detail.

  6. 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.

  7. 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. PMID:15940985

  8. Revealing ecological networks using Bayesian network inference algorithms.

    PubMed

    Milns, Isobel; Beale, Colin M; Smith, V Anne

    2010-07-01

    Understanding functional relationships within ecological networks can help reveal keys to ecosystem stability or fragility. Revealing these relationships is complicated by the difficulties of isolating variables or performing experimental manipulations within a natural ecosystem, and thus inferences are often made by matching models to observational data. Such models, however, require assumptions-or detailed measurements-of parameters such as birth and death rate, encounter frequency, territorial exclusion, and predation success. Here, we evaluate the use of a Bayesian network inference algorithm, which can reveal ecological networks based upon species and habitat abundance alone. We test the algorithm's performance and applicability on observational data of avian communities and habitat in the Peak District National Park, United Kingdom. The resulting networks correctly reveal known relationships among habitat types and known interspecific relationships. In addition, the networks produced novel insights into ecosystem structure and identified key species with high connectivity. Thus, Bayesian networks show potential for becoming a valuable tool in ecosystem analysis. PMID:20715607

  9. Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms

    PubMed Central

    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. PMID:26221132

  10. 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. PMID:26221132

  11. 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.

  12. 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.

  13. Identification of continuous-time dynamical systems: Neural network based algorithms and parallel implementation

    SciTech Connect

    Farber, R.M.; Lapedes, A.S.; Rico-Martinez, R.; Kevrekidis, I.G.

    1993-06-01

    Time-delay mappings constructed using neural networks have proven successful performing nonlinear system identification; however, because of their discrete nature, their use in bifurcation analysis of continuous-tune systems is limited. This shortcoming can be avoided by embedding the neural networks in a training algorithm that mimics a numerical integrator. Both explicit and implicit integrators can be used. The former case is based on repeated evaluations of the network in a feedforward implementation; the latter relies on a recurrent network implementation. Here the algorithms and their implementation on parallel machines (SIMD and MIMD architectures) are discussed.

  14. Identification of continuous-time dynamical systems: Neural network based algorithms and parallel implementation

    SciTech Connect

    Farber, R.M.; Lapedes, A.S. ); Rico-Martinez, R.; Kevrekidis, I.G. . Dept. of Chemical Engineering)

    1993-01-01

    Time-delay mappings constructed using neural networks have proven successful performing nonlinear system identification; however, because of their discrete nature, their use in bifurcation analysis of continuous-tune systems is limited. This shortcoming can be avoided by embedding the neural networks in a training algorithm that mimics a numerical integrator. Both explicit and implicit integrators can be used. The former case is based on repeated evaluations of the network in a feedforward implementation; the latter relies on a recurrent network implementation. Here the algorithms and their implementation on parallel machines (SIMD and MIMD architectures) are discussed.

  15. 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.

  16. 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.

  17. An intelligent service-based network architecture for wearable robots.

    PubMed

    Lee, Ka Keung; Zhang, Ping; Xu, Yangsheng; Liang, Bin

    2004-08-01

    We are developing a novel robot concept called the wearable robot. Wearable robots are mobile information devices capable of supporting remote communication and intelligent interaction between networked entities. In this paper, we explore the possible functions of such a robotic network and will present a distributed network architecture based on service components. In order to support the interaction and communication between the components in the wearable robot system, we have developed an intelligent network architecture. This service-based architecture involves three major mechanisms. The first mechanism involves the use of a task coordinator service such that the execution of the services can be managed using a priority queue. The second mechanism enables the system to automatically push the required service proxy to the client intelligently based on certain system-related conditions. In the third mechanism, we allow the system to automatically deliver services based on contextual information. Using a fuzzy-logic-based decision making system, the matching service can determine whether the service should be automatically delivered utilizing the information provided by the service, client, lookup service, and context sensors. An application scenario has been implemented to demonstrate the feasibility of this distributed service-based robot architecture. The architecture is implemented as extensions to the Jini network model. PMID:15462452

  18. 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.

  19. Neural network fusion capabilities for efficient implementation of tracking algorithms

    NASA Astrophysics Data System (ADS)

    Sundareshan, Malur K.; Amoozegar, Farid

    1996-05-01

    The ability to efficiently fuse information of different forms for facilitating intelligent decision-making is one of the major capabilities of trained multilayer neural networks that is being recognized int eh recent times. While development of innovative adaptive control algorithms for nonlinear dynamical plants which attempt to exploit these capabilities seems to be more popular, a corresponding development of nonlinear estimation algorithms using these approaches, particularly for application in target surveillance and guidance operations, has not received similar attention. In this paper we describe the capabilities and functionality of neural network algorithms for data fusion and implementation of nonlinear tracking filters. For a discussion of details and for serving as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes form the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. Such an approach results in an overall nonlinear tracking filter which has several advantages over the popular efforts at designing nonlinear estimation algorithms for tracking applications, the principle one being the reduction of mathematical and computational complexities. A system architecture that efficiently integrates the processing capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described in this paper.

  20. 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.

  1. 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.

  2. Distributed control architecture of high-speed networks

    NASA Astrophysics Data System (ADS)

    Cidon, Israel; Gopal, Inder; Kaplan, Marc A.; Kutten, Shay

    1995-05-01

    A control architecture for a high-speed packet-switched network is described. The architecture was designed and implemented as part of the PARIS (subsequently plaNET and BBNS) networking project at IBM. This high bandwidth network for integrated communication (data, voice, video) is currently operational as a laboratory prototype. It will also be deployed within the AURORA Testbed that is part of the NSF/DARPA gigabit networking program. The high bandwidth dictates the need for specialized hardware to support faster packet handling for both point-to-point and multicast connections. A faster and more efficient network control is also required in order to support the increased number of connections and their changing requirements with time. The new network control architecture presented exploits specialized hardware, thereby enabling tasks to be performed faster and with less computation overhead. In particular, since control information can be distributed quickly using hardware packet handling mechanisms, decisions can be made based upon more complete and accurate information. In some respects, this has the effect of having the benefits of centralized control (e.g., easier bandwidth resource allocation to connections), while retaining the fault tolerance and scalability of a distributed architecture.

  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. PMID:21376531

  4. 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. PMID:11665783

  5. 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.

  6. Design Guidelines for New Generation Network Architecture

    NASA Astrophysics Data System (ADS)

    Harai, Hiroaki; Fujikawa, Kenji; Kafle, Ved P.; Miyazawa, Takaya; Murata, Masayuki; Ohnishi, Masaaki; Ohta, Masataka; Umezawa, Takeshi

    Limitations are found in the recent Internet because a lot of functions and protocols are patched to the original suite of layered protocols without considering global optimization. This reveals that end-to-end argument in the original Internet was neither sufficient for the current societal network and nor for a sustainable network of the future. In this position paper, we present design guidelines for a future network, which we call the New Generation Network, which provides the inclusion of diverse human requirements, reliable connection between the real-world and virtual network space, and promotion of social potentiality for human emergence. The guidelines consist of the crystal synthesis, the reality connection, and the sustainable & evolutional guidelines.

  7. 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).

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. Computational architecture of the yeast regulatory network

    NASA Astrophysics Data System (ADS)

    Maslov, Sergei; Sneppen, Kim

    2005-12-01

    The topology of regulatory networks contains clues to their overall design principles and evolutionary history. We find that while in- and out-degrees of a given protein in the regulatory network are not correlated with each other, there exists a strong negative correlation between the out-degree of a regulatory protein and in-degrees of its targets. Such correlation positions large regulatory modules on the periphery of the network and makes them rather well separated from each other. We also address the question of relative importance of different classes of proteins quantified by the lethality of null-mutants lacking one of them as well as by the level of their evolutionary conservation. It was found that in the yeast regulatory network highly connected proteins are in fact less important than their low-connected counterparts.

  13. Algorithm For A Self-Growing Neural Network

    NASA Technical Reports Server (NTRS)

    Cios, Krzysztof J.

    1996-01-01

    CID3 algorithm simulates self-growing neural network. Constructs decision trees equivalent to hidden layers of neural network. Based on ID3 algorithm, which dynamically generates decision tree while minimizing entropy of information. CID3 algorithm generates feedforward neural network by use of either crisp or fuzzy measure of entropy.

  14. 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. PMID:16121722

  15. The New Algorithm for Symbolic Network Analysis.

    NASA Astrophysics Data System (ADS)

    Chow, John Tsai-Chiang

    A new and highly efficient tree identification algorithm is derived here for obtaining the determinant and the cofactors of a circuit's node admittance matrix, and hence, for obtaining various symbolic network functions for one-port and two-port reciprocal and nonreciprocal networks, with the network's topological description as its input. The algorithm is so devised that it is practically memory-storage free, and it is simple enough that even a microcomputer can obtain symbolic network functions for a fairly large circuit in a reasonably short time. It is worth noting that the algorithm can handle topological branches with infinite admittance values. Making use of this special feature, we have derived a simple topological model for the ideal operational amplifier, hence providing the ability to obtain various topological formulas of operational amplifier circuits in a reasonable time. By choosing appropriate symbolic network functions, along with some measured transfer function data, the circuit's nominal element values, and a nonlinear-equation solving subroutine, we have constructed a computer program to perform analog circuit fault diagnosis. This program can identify which of a circuit's elements are faulty or out of design tolerances. In the course of this research we have also identified an application to a biological problem, one in which the resistor values of an electrical model of the guinea-pig cochlea can easily be deduced even when some nodes are inaccessible for measurements. All these features have been implemented on a very modest microcomputer, the Apple II. Obviously, a larger computer will not only accomplish the same result faster but also it will be capable of analyzing much larger circuits.

  16. 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.

  17. Network architectures in support of digital subscriber line (DSL) deployment

    NASA Astrophysics Data System (ADS)

    Peuch, Bruno

    1998-09-01

    DSL technology enables very high bandwidth transmission in a point-to-point fashion from a customer's premises to a central office (CO), wiring center, or other logical point of traffic aggregation. Unlike many technologies that enable broadband Internet access, DSL technology does not determine a specific architecture to be deployed at either the customer's premises or in the service/access provider's network. In fact, DSL technology can be used in conjunction with a variety of network architectures. While being agnostic regarding to higher-layer protocols, there are still several critical 'protocol specific' issues that need to be addressed when deploying DSL as a solution for IP (Internet/intrAnet) access. This paper will address these issues and present a range of network architectures that incorporate DSL technology. This paper will only focus on those architectures that enable IP access. These architectures are divided into three categories: Traditional Dialled Model (TDM), frame-based (Frame Relay/Ethernet), and cell-based (ATM).

  18. Natural gradient learning algorithms for RBF networks.

    PubMed

    Zhao, Junsheng; Wei, Haikun; Zhang, Chi; Li, Weiling; Guo, Weili; Zhang, Kanjian

    2015-02-01

    Radial basis function (RBF) networks are one of the most widely used models for function approximation and classification. There are many strange behaviors in the learning process of RBF networks, such as slow learning speed and the existence of the plateaus. The natural gradient learning method can overcome these disadvantages effectively. It can accelerate the dynamics of learning and avoid plateaus. In this letter, we assume that the probability density function (pdf) of the input and the activation function are gaussian. First, we introduce natural gradient learning to the RBF networks and give the explicit forms of the Fisher information matrix and its inverse. Second, since it is difficult to calculate the Fisher information matrix and its inverse when the numbers of the hidden units and the dimensions of the input are large, we introduce the adaptive method to the natural gradient learning algorithms. Finally, we give an explicit form of the adaptive natural gradient learning algorithm and compare it to the conventional gradient descent method. Simulations show that the proposed adaptive natural gradient method, which can avoid the plateaus effectively, has a good performance when RBF networks are used for nonlinear functions approximation. PMID:25380332

  19. Special Issue on a Fault Tolerant Network on Chip Architecture

    NASA Astrophysics Data System (ADS)

    Janidarmian, Majid; Tinati, Melika; Khademzadeh, Ahmad; Ghavibazou, Maryam; Fekr, Atena Roshan

    2010-06-01

    In this paper a fast and efficient spare switch selection algorithm is presented in a reliable NoC architecture based on specific application mapped onto mesh topology called FERNA. Based on ring concept used in FERNA, this algorithm achieves best results equivalent to exhaustive algorithm with much less run time improving two parameters. Inputs of FERNA algorithm for response time of the system and extra communication cost minimization are derived from simulation of high transaction level using SystemC TLM and mathematical formulation, respectively. The results demonstrate that improvement of above mentioned parameters lead to advance whole system reliability that is analytically calculated. Mapping algorithm has been also investigated as an effective issue on extra bandwidth requirement and system reliability.

  20. 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.

  1. 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

  2. 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.

  3. Large-Scale Networked Virtual Environments: Architecture and Applications

    ERIC Educational Resources Information Center

    Lamotte, Wim; Quax, Peter; Flerackers, Eddy

    2008-01-01

    Purpose: Scalability is an important research topic in the context of networked virtual environments (NVEs). This paper aims to describe the ALVIC (Architecture for Large-scale Virtual Interactive Communities) approach to NVE scalability. Design/methodology/approach: The setup and results from two case studies are shown: a 3-D learning environment…

  4. Investigation of network architecture development and properties in thermoset matrices

    NASA Astrophysics Data System (ADS)

    Swanson, Jeremy Owen

    Matrices employed in composite materials directly influence overall composite properties. In all thermoset materials, molecular level interactions and transformations during cure result in heterogeneous architecture. Variability in connectivity results from the often dramatic spatial and topological changes that occur during the crosslinking process. Compatibility (fillers, pigments, additives), temperature gradients and reactivity differences in the precursors only serve to increase the complexity of network formation. The objective of the research herein is to characterize and understand the relationships between cure conditions, conversion, connectivity, network architecture and properties in glassy thermosetting matrix resins. In this research, epoxy and vinyl ester resins (VERs) were characterized to identify controlling factors in the development of network architecture and understand how they affect the mechanical properties. VERs cure under low temperature conditions (< 50°C) via redox catalysis resulted in vitrification limiting conversion with resulting glass transition temperatures (Tgs) approximately 15°C above the cure temperature. Subsequently, in situ ligand exchange altered the activity of the metal catalyst, and the reduced connectivity of the resulting networks translated into a 30% reduction in stiffness above Tg. Network architecture was further manipulated by changing the chemical composition of the backbone. Incorporation of POSS nanoparticles into VERs resulted in changes to initial network development, with higher levels of conversion prior to vitrification. 3,3'-DDS was cured with a variety of epoxies and examined for conversion, connectivity and mechanical properties. Comparison with 4,4'-DDS revealed significant correlations between molecular level structure and properties. The research established relationships between cure conditions, conversion, connectivity and properties in glassy thermosetting matrix resins. Specifically, the

  5. 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

  6. 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

  7. 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…

  8. Localization Algorithms of Underwater Wireless Sensor Networks: A Survey

    PubMed Central

    Han, Guangjie; Jiang, Jinfang; Shu, Lei; Xu, Yongjun; Wang, Feng

    2012-01-01

    In Underwater Wireless Sensor Networks (UWSNs), localization is one of most important technologies since it plays a critical role in many applications. Motivated by widespread adoption of localization, in this paper, we present a comprehensive survey of localization algorithms. First, we classify localization algorithms into three categories based on sensor nodes’ mobility: stationary localization algorithms, mobile localization algorithms and hybrid localization algorithms. Moreover, we compare the localization algorithms in detail and analyze future research directions of localization algorithms in UWSNs. PMID:22438752

  9. 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.

  10. 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.

  11. 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.

  12. 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. PMID:26595445

  13. 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.

  14. Multigrid algorithms for tensor network states.

    PubMed

    Dolfi, Michele; Bauer, Bela; Troyer, Matthias; Ristivojevic, Zoran

    2012-07-13

    The widely used density matrix renormalization group (DRMG) method often fails to converge in systems with multiple length scales, such as lattice discretizations of continuum models and dilute or weakly doped lattice models. The local optimization employed by DMRG to optimize the wave function is ineffective in updating large-scale features. Here we present a multigrid algorithm that solves these convergence problems by optimizing the wave function at different spatial resolutions. We demonstrate its effectiveness by simulating bosons in continuous space and study nonadiabaticity when ramping up the amplitude of an optical lattice. The algorithm can be generalized to tensor network methods and combined with the contractor renormalization group method to study dilute and weakly doped lattice models. PMID:23030148

  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. Resting state networks' corticotopy: the dual intertwined rings architecture.

    PubMed

    Mesmoudi, Salma; Perlbarg, Vincent; Rudrauf, David; Messe, Arnaud; Pinsard, Basile; Hasboun, Dominique; Cioli, Claudia; Marrelec, Guillaume; Toro, Roberto; Benali, Habib; Burnod, Yves

    2013-01-01

    How does the brain integrate multiple sources of information to support normal sensorimotor and cognitive functions? To investigate this question we present an overall brain architecture (called "the dual intertwined rings architecture") that relates the functional specialization of cortical networks to their spatial distribution over the cerebral cortex (or "corticotopy"). Recent results suggest that the resting state networks (RSNs) are organized into two large families: 1) a sensorimotor family that includes visual, somatic, and auditory areas and 2) a large association family that comprises parietal, temporal, and frontal regions and also includes the default mode network. We used two large databases of resting state fMRI data, from which we extracted 32 robust RSNs. We estimated: (1) the RSN functional roles by using a projection of the results on task based networks (TBNs) as referenced in large databases of fMRI activation studies; and (2) relationship of the RSNs with the Brodmann Areas. In both classifications, the 32 RSNs are organized into a remarkable architecture of two intertwined rings per hemisphere and so four rings linked by homotopic connections. The first ring forms a continuous ensemble and includes visual, somatic, and auditory cortices, with interspersed bimodal cortices (auditory-visual, visual-somatic and auditory-somatic, abbreviated as VSA ring). The second ring integrates distant parietal, temporal and frontal regions (PTF ring) through a network of association fiber tracts which closes the ring anatomically and ensures a functional continuity within the ring. The PTF ring relates association cortices specialized in attention, language and working memory, to the networks involved in motivation and biological regulation and rhythms. This "dual intertwined architecture" suggests a dual integrative process: the VSA ring performs fast real-time multimodal integration of sensorimotor information whereas the PTF ring performs multi

  17. MIRA: mutual information-based reporter algorithm for metabolic networks

    PubMed Central

    Cicek, A. Ercument; Roeder, Kathryn; Ozsoyoglu, Gultekin

    2014-01-01

    Motivation: Discovering the transcriptional regulatory architecture of the metabolism has been an important topic to understand the implications of transcriptional fluctuations on metabolism. The reporter algorithm (RA) was proposed to determine the hot spots in metabolic networks, around which transcriptional regulation is focused owing to a disease or a genetic perturbation. Using a z-score-based scoring scheme, RA calculates the average statistical change in the expression levels of genes that are neighbors to a target metabolite in the metabolic network. The RA approach has been used in numerous studies to analyze cellular responses to the downstream genetic changes. In this article, we propose a mutual information-based multivariate reporter algorithm (MIRA) with the goal of eliminating the following problems in detecting reporter metabolites: (i) conventional statistical methods suffer from small sample sizes, (ii) as z-score ranges from minus to plus infinity, calculating average scores can lead to canceling out opposite effects and (iii) analyzing genes one by one, then aggregating results can lead to information loss. MIRA is a multivariate and combinatorial algorithm that calculates the aggregate transcriptional response around a metabolite using mutual information. We show that MIRA’s results are biologically sound, empirically significant and more reliable than RA. Results: We apply MIRA to gene expression analysis of six knockout strains of Escherichia coli and show that MIRA captures the underlying metabolic dynamics of the switch from aerobic to anaerobic respiration. We also apply MIRA to an Autism Spectrum Disorder gene expression dataset. Results indicate that MIRA reports metabolites that highly overlap with recently found metabolic biomarkers in the autism literature. Overall, MIRA is a promising algorithm for detecting metabolic drug targets and understanding the relation between gene expression and metabolic activity. Availability and

  18. 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.

  19. 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.

  20. 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.

  1. A fault-tolerant network architecture for integrated avionics

    NASA Technical Reports Server (NTRS)

    Butler, Bryan; Adams, Stuart

    1991-01-01

    The Army Fault-Tolerant Architecture (AFTA) under construction at the Charles Stark Draper Laboratory is an example of a highly integrated critical avionics system. The AFTA system must connect to other redundant and nonredundant systems, as well as to input/output devices. A fault-tolerant data bus (FTDB) is being developed to provide highly reliable communication between the AFTA computer and other network stations. The FTDB is being designed for Byzantine resilience and is probably capable of tolerating any single arbitrary fault. The author describes a prototype architecture for the fault-tolerant data bus.

  2. Global optimization algorithm for heat exchanger networks

    SciTech Connect

    Quesada, I.; Grossmann, I.E. )

    1993-03-01

    This paper deals with the global optimization of heat exchanger networks with fixed topology. It is shown that if linear area cost functions are assumed, as well as arithmetic mean driving force temperature differences in networks with isothermal mixing, the corresponding nonlinear programming (NLP) optimization problem involves linear constraints and a sum of linear fractional functions in the objective which are nonconvex. A rigorous algorithm is proposed that is based on a convex NLP underestimator that involves linear and nonlinear estimators for fractional and bilinear terms which provide a tight lower bound to the global optimum. This NLP problem is used within a spatial branch and bound method for which branching rules are given. Basic properties of the proposed method are presented, and its application is illustrated with several example problems. The results show that the proposed method only requires few nodes in the branch and bound search.

  3. Architectural Exploration and Design Methodologies of Photonic Interconnection Networks

    NASA Astrophysics Data System (ADS)

    Chan, Jong Wu

    Photonic technology is becoming an increasingly attractive solution to the problems facing today's electronic chip-scale interconnection networks. Recent progress in silicon photonics research has enabled the demonstration of all the necessary optical building blocks for creating extremely high-bandwidth density and energy-efficient links for on- and off-chip communications. From the feasibility and architecture perspective however, photonics represents a dramatic paradigm shift from traditional electronic network designs due to fundamental differences in how electronics and photonics function and behave. As a result of these differences, new modeling and analysis methods must be employed in order to properly realize a functional photonic chip-scale interconnect design. In this work, we present a methodology for characterizing and modeling fundamental photonic building blocks which can subsequently be combined to form full photonic network architectures. We also describe a set of tools which can be utilized to assess the physical-layer and system-level performance properties of a photonic network. The models and tools are integrated in a novel open-source design and simulation environment called PhoenixSim. Next, we leverage PhoenixSim for the study of chip-scale photonic networks. We examine several photonic networks through the synergistic study of both physical-layer metrics and system-level metrics. This holistic analysis method enables us to provide deeper insight into architecture scalability since it considers insertion loss, crosstalk, and power dissipation. In addition to these novel physical-layer metrics, traditional system-level metrics of bandwidth and latency are also obtained. Lastly, we propose a novel routing architecture known as wavelength-selective spatial routing. This routing architecture is analogous to electronic virtual channels since it enables the transmission of multiple logical optical channels through a single physical plane (i.e. the

  4. An Architecture for Dynamic Trust Monitoring in Mobile Networks

    NASA Astrophysics Data System (ADS)

    Onolaja, Olufunmilola; Bahsoon, Rami; Theodoropoulos, Georgios

    Collusion attacks remain a major problem of reputation and trust models, in mobile ad hoc networks. By covering up malicious behaviour of one another from the remaining part of the network, two or more malicious nodes may collaborate to cause damage to or disrupt the network. A number of models exist, which have been proposed to address this issue. Despite these however, the assurance of trusted communication still remains a challenge in these networks. We present a dynamic trust model that detects malicious behaviour at runtime and prevents collusion attacks. Our proposed model employs a novel approach that has the advantage of predicting the future trustworthiness of nodes, based on historical and online behaviour of nodes. This is achieved by an architecture that applies the paradigm of Dynamic Data Driven Application Systems, in solving the problem of collusion attacks in mobile networks.

  5. 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

  6. 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

  7. Elastic Optical Path Network Architecture: Framework for Spectrally-Efficient and Scalable Future Optical Networks

    NASA Astrophysics Data System (ADS)

    Jinno, Masahiko; Takara, Hidehiko; Sone, Yoshiaki; Yonenaga, Kazushige; Hirano, Akira

    This paper presents an elastic optical path network architecture as a novel networking framework to address the looming capacity crunch problem in internet protocol (IP) and optical networks. The basic idea is to introduce elasticity and adaptation into the optical domain to yield spectrally-efficient optical path accommodation, heightened network scalability through IP traffic offloading to the elastic optical layer, and enhanced survivability for serious disasters.

  8. ISINA: INTEGRAL Source Identification Network Algorithm

    NASA Astrophysics Data System (ADS)

    Scaringi, S.; Bird, A. J.; Clark, D. J.; Dean, A. J.; Hill, A. B.; McBride, V. A.; Shaw, S. E.

    2008-11-01

    We give an overview of ISINA: INTEGRAL Source Identification Network Algorithm. This machine learning algorithm, using random forests, is applied to the IBIS/ISGRI data set in order to ease the production of unbiased future soft gamma-ray source catalogues. First, we introduce the data set and the problems encountered when dealing with images obtained using the coded mask technique. The initial step of source candidate searching is introduced and an initial candidate list is created. A description of the feature extraction on the initial candidate list is then performed together with feature merging for these candidates. Three training and testing sets are created in order to deal with the diverse time-scales encountered when dealing with the gamma-ray sky. Three independent random forests are built: one dealing with faint persistent source recognition, one dealing with strong persistent sources and a final one dealing with transients. For the latter, a new transient detection technique is introduced and described: the transient matrix. Finally the performance of the network is assessed and discussed using the testing set and some illustrative source examples. Based on observations with INTEGRAL, an ESA project with instruments and science data centre funded by ESA member states (especially the PI countries: Denmark, France, Germany, Italy, Spain), Czech Republic and Poland, and the participation of Russia and the USA. E-mail: simo@astro.soton.ac.uk

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

    NASA Astrophysics Data System (ADS)

    Stoughton, John W.; Mielke, Roland R.

    1988-02-01

    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.

  10. 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.

  11. 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.

  12. 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.

  13. A constructive algorithm for training cooperative neural network ensembles.

    PubMed

    Islam, Md M; Yao, Xin; Murase, K

    2003-01-01

    Presents a constructive algorithm for training cooperative neural-network ensembles (CNNEs). CNNE combines ensemble architecture design with cooperative training for individual neural networks (NNs) in ensembles. Unlike most previous studies on training ensembles, CNNE puts emphasis on both accuracy and diversity among individual NNs in an ensemble. In order to maintain accuracy among individual NNs, the number of hidden nodes in individual NNs are also determined by a constructive approach. Incremental training based on negative correlation is used in CNNE to train individual NNs for different numbers of training epochs. The use of negative correlation learning and different training epochs for training individual NNs reflect CNNEs emphasis on diversity among individual NNs in an ensemble. CNNE has been tested extensively on a number of benchmark problems in machine learning and neural networks, including Australian credit card assessment, breast cancer, diabetes, glass, heart disease, letter recognition, soybean, and Mackey-Glass time series prediction problems. The experimental results show that CNNE can produce NN ensembles with good generalization ability. PMID:18238062

  14. 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

  15. 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.

  16. Box-covering algorithm for fractal dimension of weighted networks

    NASA Astrophysics Data System (ADS)

    Wei, Dai-Jun; Liu, Qi; Zhang, Hai-Xin; Hu, Yong; Deng, Yong; Mahadevan, Sankaran

    2013-10-01

    Box-covering algorithm is a widely used method to measure the fractal dimension of complex networks. Existing researches mainly deal with the fractal dimension of unweighted networks. Here, the classical box covering algorithm is modified to deal with the fractal dimension of weighted networks. Box size length is obtained by accumulating the distance between two nodes connected directly and graph-coloring algorithm is based on the node strength. The proposed method is applied to calculate the fractal dimensions of the ``Sierpinski'' weighted fractal networks, the E.coli network, the Scientific collaboration network, the C.elegans network and the USAir97 network. Our results show that the proposed method is efficient when dealing with the fractal dimension problem of complex networks. We find that the fractal property is influenced by the edge-weight in weighted networks. The possible variation of fractal dimension due to changes in edge-weights of weighted networks is also discussed.

  17. Box-covering algorithm for fractal dimension of weighted networks.

    PubMed

    Wei, Dai-Jun; Liu, Qi; Zhang, Hai-Xin; Hu, Yong; Deng, Yong; Mahadevan, Sankaran

    2013-01-01

    Box-covering algorithm is a widely used method to measure the fractal dimension of complex networks. Existing researches mainly deal with the fractal dimension of unweighted networks. Here, the classical box covering algorithm is modified to deal with the fractal dimension of weighted networks. Box size length is obtained by accumulating the distance between two nodes connected directly and graph-coloring algorithm is based on the node strength. The proposed method is applied to calculate the fractal dimensions of the "Sierpinski" weighted fractal networks, the E.coli network, the Scientific collaboration network, the C.elegans network and the USAir97 network. Our results show that the proposed method is efficient when dealing with the fractal dimension problem of complex networks. We find that the fractal property is influenced by the edge-weight in weighted networks. The possible variation of fractal dimension due to changes in edge-weights of weighted networks is also discussed. PMID:24157896

  18. 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

  19. A cross-layer optimization algorithm for wireless sensor network

    NASA Astrophysics Data System (ADS)

    Wang, Yan; Liu, Le Qing

    2010-07-01

    Energy is critical for typical wireless sensor networks (WSN) and how to energy consumption and maximize network lifetime are big challenges for Wireless sensor networks; cross layer algorithm is main method to solve this problem. In this paper, firstly, we analyze current layer-based optimal methods in wireless sensor network and summarize the physical, link and routing optimization techniques. Secondly we compare some strategies in cross-layer optimization algorithms. According to the analysis and summary of the current lifetime algorithms in wireless sensor network A cross layer optimization algorithm is proposed,. Then this optimization algorithm proposed in the paper is adopted to improve the traditional Leach routing protocol. Simulation results show that this algorithm is an excellent cross layer algorithm for reducing energy consumption.

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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. PMID:22303172

  5. 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.

  6. A novel waveband routing algorithm in hierarchical WDM optical networks

    NASA Astrophysics Data System (ADS)

    Huang, Jun; Guo, Xiaojin; Qiu, Shaofeng; Luo, Jiangtao; Zhang, Zhizhong

    2007-11-01

    Hybrid waveband/wavelength switching in intelligent optical networks is gaining more and more academic attention. It is very challenging to develop efficient algorithms to efficiently use waveband switching capability. In this paper, we propose a novel cross-layer routing algorithm, waveband layered graph routing algorithm (WBLGR), in waveband switching-enabled optical networks. Through extensive simulation WBLGR algorithm can significantly improve the performance in terms of reduced call blocking probability.

  7. 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.

  8. A Comparative Analysis of Community Detection Algorithms on Artificial Networks.

    PubMed

    Yang, Zhao; Algesheimer, René; Tessone, Claudio J

    2016-01-01

    Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms' computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm's predicting power and the effective computing time. PMID:27476470

  9. 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.

  10. Fast algorithm for detecting community structure in networks

    NASA Astrophysics Data System (ADS)

    Newman, M. E.

    2004-06-01

    Many networks display community structure—groups of vertices within which connections are dense but between which they are sparser—and sensitive computer algorithms have in recent years been developed for detecting this structure. These algorithms, however, are computationally demanding, which limits their application to small networks. Here we describe an algorithm which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster, than previous algorithms. We give several example applications, including one to a collaboration network of more than 50 000 physicists.

  11. Architecture, Infrastructure, and Broadband Civic Network Design: An Institutional View

    NASA Astrophysics Data System (ADS)

    Venkatesh, Murali; Chango, Mawaki

    Cultural values frame architectures, and architectures motivate infrastructures — by which we mean the foundational telecommunications and Internet access services that software applications depend on. Design is the social process that realizes architectural elements in an infrastructure. This process is often a conflicted one where transformative visions confront the realities of entrenched power, where innovation confronts pressure from institutionalized interests and practices working to resist change and reproduce the status quo in the design outcome. We use this viewpoint to discuss design aspects of the Urban-net, a broadband civic networking case. Civic networks are embodiments of distinctive technological configurations and forms of social order. In choosing some technological configurations over others, designers are favoring some social structural configurations over alternatives. To the extent that a civic network sets out to reconfigure the prevailing social order (as was the case in the Urban-net project considered here), the design process becomes the arena where challengers of the prevailing order encounter its defenders. In this case, the defenders prevailed and the design that emerged was conservative and reproduced the status quo. What steps can stakeholders take so that the project’s future development is in line with the original aim of structural change? We outline two strategies. We argue the importance of articulating cultural desiderata in an architecture that stakeholders can use to open up the infrastructure to new constituents and incremental change. Next, we argue the importance of designing the conditions of design. The climate in which social interactions occur can powerfully shape design outcomes, but this does not usually figure in stakeholders’ design concerns.

  12. Massive spatial data network service architecture based on double-cluster

    NASA Astrophysics Data System (ADS)

    Li, Zhongmin; Gao, Lu

    2009-10-01

    It is the tendency for the development of massive spatial data network service to use cluster to enlarge load capacity of spatial data server. In this paper, we use the OSD (Object-based Storage Device) storage cluster as the shared storage of LVS (Linux Virtual Server) server cluster, and use the servers in the server pool of the LVS server cluster as the storage client of the OSD storage cluster, to build a scalable massive spatial data network service architecture, which uses the high scalability of the LVS server cluster and the OSD storage cluster to avoid the bottlenecks of massive spatial data network service bandwidth and storage I/O throughput. Several load balance scheduling algorithms embedded in the LVS server cluster can satisfy the demand of load balance in many applications. But those algorithms can't optimize load balance of spatial data servers, regardless of the features of spatial data. Spatial data in large scale network service application is generally organized according to the global longitude and latitude, and managed according to the principle "vertical hierarchies and horizontal dividing". According to the features of spatial data, we optimize the scheduling algorithm to enhance the Cache utilization efficiency for single spatial data server.

  13. 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.

  14. A Cross Unequal Clustering Routing Algorithm for Sensor Network

    NASA Astrophysics Data System (ADS)

    Tong, Wang; Jiyi, Wu; He, Xu; Jinghua, Zhu; Munyabugingo, Charles

    2013-08-01

    In the routing protocol for wireless sensor network, the cluster size is generally fixed in clustering routing algorithm for wireless sensor network, which can easily lead to the "hot spot" problem. Furthermore, the majority of routing algorithms barely consider the problem of long distance communication between adjacent cluster heads that brings high energy consumption. Therefore, this paper proposes a new cross unequal clustering routing algorithm based on the EEUC algorithm. In order to solve the defects of EEUC algorithm, this algorithm calculating of competition radius takes the node's position and node's remaining energy into account to make the load of cluster heads more balanced. At the same time, cluster adjacent node is applied to transport data and reduce the energy-loss of cluster heads. Simulation experiments show that, compared with LEACH and EEUC, the proposed algorithm can effectively reduce the energy-loss of cluster heads and balance the energy consumption among all nodes in the network and improve the network lifetime

  15. A New Integrated Neural Network Architecture for Streamflow Forecasting

    NASA Astrophysics Data System (ADS)

    Teegavarapu, R. S.

    2005-12-01

    Streamflow time series often provide valuable insights into the underlying physical processes that govern response of any watershed. Patterns derived from time series based on repeated structures within these series can be beneficial for developing new or improved data-driven forecasting models. Data-driven models, artificial neural networks (ANN), are developed in the current study for streamflow prediction using input structures that are classified into geometrically similar patterns. The number of patterns that are identified in a series depends on the lagged values of streamflows used in the input structures of the ANN model. A new modular and integrated ANN architecture that combines several ANN models, referred to as pattern-classified neural network (PCNN), is proposed, developed and investigated in this study. The ANN models are used for one step-ahead prediction of streamflows for Reed Creek and Little River, Virginia. Results obtained from this study suggest that the use of these patterns in the process of training has improved the performance of the neural networks in prediction. The improved performance of the ANN models can be attributed to prior classification of data, which in a way has complimented and enhanced the already existing classification abilities of the neural networks. The PCNN architecture also provides the benefit of better generalization of a data-driven model by developing several independent models instead of one global data-driven prediction model for the entire data.

  16. 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

  17. 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.

  18. 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

  19. 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. PMID:21294627

  20. 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.

  1. An efficient optical architecture for sparsely connected neural networks

    NASA Technical Reports Server (NTRS)

    Hine, Butler P., III; Downie, John D.; Reid, Max B.

    1990-01-01

    An architecture for general-purpose optical neural network processor is presented in which the interconnections and weights are formed by directing coherent beams holographically, thereby making use of the space-bandwidth products of the recording medium for sparsely interconnected networks more efficiently that the commonly used vector-matrix multiplier, since all of the hologram area is in use. An investigation is made of the use of computer-generated holograms recorded on such updatable media as thermoplastic materials, in order to define the interconnections and weights of a neural network processor; attention is given to limits on interconnection densities, diffraction efficiencies, and weighing accuracies possible with such an updatable thin film holographic device.

  2. 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.

  3. Algorithms and architectures for adaptive least squares signal processing, with applications in magnetoencephalography

    SciTech Connect

    Lewis, P.S.

    1988-10-01

    Least squares techniques are widely used in adaptive signal processing. While algorithms based on least squares are robust and offer rapid convergence properties, they also tend to be complex and computationally intensive. To enable the use of least squares techniques in real-time applications, it is necessary to develop adaptive algorithms that are efficient and numerically stable, and can be readily implemented in hardware. The first part of this work presents a uniform development of general recursive least squares (RLS) algorithms, and multichannel least squares lattice (LSL) algorithms. RLS algorithms are developed for both direct estimators, in which a desired signal is present, and for mixed estimators, in which no desired signal is available, but the signal-to-data cross-correlation is known. In the second part of this work, new and more flexible techniques of mapping algorithms to array architectures are presented. These techniques, based on the synthesis and manipulation of locally recursive algorithms (LRAs), have evolved from existing data dependence graph-based approaches, but offer the increased flexibility needed to deal with the structural complexities of the RLS and LSL algorithms. Using these techniques, various array architectures are developed for each of the RLS and LSL algorithms and the associated space/time tradeoffs presented. In the final part of this work, the application of these algorithms is demonstrated by their employment in the enhancement of single-trial auditory evoked responses in magnetoencephalography. 118 refs., 49 figs., 36 tabs.

  4. 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

  5. Resting State Networks' Corticotopy: The Dual Intertwined Rings Architecture

    PubMed Central

    Mesmoudi, Salma; Perlbarg, Vincent; Rudrauf, David; Messe, Arnaud; Pinsard, Basile; Hasboun, Dominique; Cioli, Claudia; Marrelec, Guillaume; Toro, Roberto; Benali, Habib; Burnod, Yves

    2013-01-01

    How does the brain integrate multiple sources of information to support normal sensorimotor and cognitive functions? To investigate this question we present an overall brain architecture (called “the dual intertwined rings architecture”) that relates the functional specialization of cortical networks to their spatial distribution over the cerebral cortex (or “corticotopy”). Recent results suggest that the resting state networks (RSNs) are organized into two large families: 1) a sensorimotor family that includes visual, somatic, and auditory areas and 2) a large association family that comprises parietal, temporal, and frontal regions and also includes the default mode network. We used two large databases of resting state fMRI data, from which we extracted 32 robust RSNs. We estimated: (1) the RSN functional roles by using a projection of the results on task based networks (TBNs) as referenced in large databases of fMRI activation studies; and (2) relationship of the RSNs with the Brodmann Areas. In both classifications, the 32 RSNs are organized into a remarkable architecture of two intertwined rings per hemisphere and so four rings linked by homotopic connections. The first ring forms a continuous ensemble and includes visual, somatic, and auditory cortices, with interspersed bimodal cortices (auditory-visual, visual-somatic and auditory-somatic, abbreviated as VSA ring). The second ring integrates distant parietal, temporal and frontal regions (PTF ring) through a network of association fiber tracts which closes the ring anatomically and ensures a functional continuity within the ring. The PTF ring relates association cortices specialized in attention, language and working memory, to the networks involved in motivation and biological regulation and rhythms. This “dual intertwined architecture” suggests a dual integrative process: the VSA ring performs fast real-time multimodal integration of sensorimotor information whereas the PTF ring performs multi

  6. The brain's functional network architecture reveals human motives.

    PubMed

    Hein, Grit; Morishima, Yosuke; Leiberg, Susanne; Sul, Sunhae; Fehr, Ernst

    2016-03-01

    Goal-directed human behaviors are driven by motives. Motives are, however, purely mental constructs that are not directly observable. Here, we show that the brain's functional network architecture captures information that predicts different motives behind the same altruistic act with high accuracy. In contrast, mere activity in these regions contains no information about motives. Empathy-based altruism is primarily characterized by a positive connectivity from the anterior cingulate cortex (ACC) to the anterior insula (AI), whereas reciprocity-based altruism additionally invokes strong positive connectivity from the AI to the ACC and even stronger positive connectivity from the AI to the ventral striatum. Moreover, predominantly selfish individuals show distinct functional architectures compared to altruists, and they only increase altruistic behavior in response to empathy inductions, but not reciprocity inductions. PMID:26941317

  7. A Comparative Analysis of Community Detection Algorithms on Artificial Networks

    NASA Astrophysics Data System (ADS)

    Yang, Zhao; Algesheimer, René; Tessone, Claudio J.

    2016-08-01

    Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms’ computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm’s predicting power and the effective computing time.

  8. Training Feedforward Neural Networks: An Algorithm Giving Improved Generalization.

    PubMed

    Lee, Charles W.

    1997-01-01

    An algorithm is derived for supervised training in multilayer feedforward neural networks. Relative to the gradient descent backpropagation algorithm it appears to give both faster convergence and improved generalization, whilst preserving the system of backpropagating errors through the network. Copyright 1996 Elsevier Science Ltd. PMID:12662887

  9. A Comparative Analysis of Community Detection Algorithms on Artificial Networks

    PubMed Central

    Yang, Zhao; Algesheimer, René; Tessone, Claudio J.

    2016-01-01

    Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms’ computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm’s predicting power and the effective computing time. PMID:27476470

  10. Correlation Between Channel Profile and Plan View Drainage Network Architecture

    NASA Astrophysics Data System (ADS)

    Shelef, E.; Hilley, G. E.

    2011-12-01

    This research explores the relationship between the plan-view network and profile geometry of channels using high-resolution digital topography and numerical models. In particular, we study the relations between plan-view morphometrics of the channel network and the mechanics of land-shaping processes as reflected by channel profile concavity. This analysis addresses one of the long-standing questions in geomorphology relating to the mechanistic significance of various plan-view channel network geometry measures. Statistically based studies suggest that Hortonian measures of channel network architecture (e.g. bifurcation ratio, area ratio, and length ratio) describe virtually all possible network geometries, and hence are not diagnostic when evaluating the origins of the geometry of a particular network. Our analyses of high resolution DEMs that capture different channel profile concavities (i.e debris flow vs. fluvial flows), as well as the topography of landscapes produced by process-based numerical models affirms this conclusion and indicates that Hortonian measures, as well as Hack exponent, are insensitive to channel concavity. In contrast, channel frequency (number of channel segments per area) appears to provide a measure that is sensitive to channel concavity. As such, channel frequency appears to discern between landscapes dominated by different land-shaping processes that produce different channel profile concavities. In the context of headword growing networks, the observed relations between concavity and channel frequency can be modeled through the coupled effect of concavity and surface roughness on the competition between headword growing channels. Our results suggest that the plan-view geometry of channel networks does not simply arise from random deflection of channels that once joined, cannot separate, but rather reflects the underlying processes that incise through rock and transport mass through the channel network

  11. A solution for parallel network architectures applied to network defense appliances and sensors

    NASA Astrophysics Data System (ADS)

    Naber, Eric C.; Velez, Paul G.; Johal, Amanpreet S.

    2012-06-01

    Network defense has more technologies available for purchase today than ever before. As the number of threats increase, organizations are deploying multiple defense technologies to defend their networks. For instance, an enterprise network boundary often implements multiple network defense appliances, some with overlapping capabilities (e.g., firewalls, IDS/IPS, DNS Defense). These appliances are applied in a serial fashion to create a chain of network processing specifically designed to drop bad traffic from the network. In these architectures, once a packet is dropped by an appliance subsequent appliances do not process it. This introduces significant limitations; (1) Stateful appliances will maintain an internal state which differs from network reality; (2) The network manager cannot determine, or unit test, how each appliance would have treated each packet; (3) The appliance "votes" cannot be combined to achieve higherlevel functionality. To address these limitations, we have developed a novel, backwards-compatible Parallel Architecture for Network Defense Appliances (PANDA). Our approach allows every appliance to process all network traffic and cast a vote to drop or allow each packet. This "crowd-sourcing" approach allows the network designer to take full advantage of each appliance, understand how each appliance is behaving, and achieve new collaborative appliance behavior.

  12. 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.

  13. 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.

  14. 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.

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

    PubMed

    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

  16. 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

  17. Signalling design and architecture for a proposed mobile satellite network

    NASA Technical Reports Server (NTRS)

    Yan, T.-Y.; Cheng, U.; Wang, C.

    1990-01-01

    In a frequency-division/demand-assigned multiple-access (FD/DAMA) architecture, each mobile subscriber must make a connection request to the Network Management Center before transmission for either open-end or closed-end services. Open-end services are for voice calls and long file transfer and are processed on a blocked-call-cleared basis. Closed-end services are for transmitting burst data and are processed on a first-come first-served basis. This paper presents the signalling design and architecture for non-voice services of an FD/DAMA mobile satellite network. The connection requests are made through the recently proposed multiple channel collision resolution scheme which provides a significantly higher throughput than the traditional slotted ALOHA scheme. For non-voice services, it is well known that retransmissions are necessary to ensure the delivery of a message in its entirety from the source to destination. Retransmission protocols for open-end and closed-end data transfer are investigated. The signal structure for the proposed network is derived from X-25 standards with appropriate modifications. The packet types and their usages are described in this paper.

  18. Randomly evolving idiotypic networks: Structural properties and architecture

    NASA Astrophysics Data System (ADS)

    Schmidtchen, Holger; Thüne, Mario; Behn, Ulrich

    2012-07-01

    We consider a minimalistic dynamic model of the idiotypic network of B lymphocytes. A network node represents a population of B lymphocytes of the same specificity (idiotype), which is encoded by a bit string. The links of the network connect nodes with complementary and nearly complementary bit strings, allowing for a few mismatches. A node is occupied if a lymphocyte clone of the corresponding idiotype exists; otherwise it is empty. There is a continuous influx of new B lymphocytes of random idiotype from the bone marrow. B lymphocytes are stimulated by cross-linking their receptors with complementary structures. If there are too many complementary structures, steric hindrance prevents cross-linking. Stimulated cells proliferate and secrete antibodies of the same idiotype as their receptors; unstimulated lymphocytes die. Depending on few parameters, the autonomous system evolves randomly towards patterns of highly organized architecture, where the nodes can be classified into groups according to their statistical properties. We observe and describe analytically the building principles of these patterns, which make it possible to calculate number and size of the node groups and the number of links between them. The architecture of all patterns observed so far in simulations can be explained this way. A tool for real-time pattern identification is proposed.

  19. 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.

  20. 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.

  1. Adaptive clustering algorithm for community detection in complex networks.

    PubMed

    Ye, Zhenqing; Hu, Songnian; Yu, Jun

    2008-10-01

    Community structure is common in various real-world networks; methods or algorithms for detecting such communities in complex networks have attracted great attention in recent years. We introduced a different adaptive clustering algorithm capable of extracting modules from complex networks with considerable accuracy and robustness. In this approach, each node in a network acts as an autonomous agent demonstrating flocking behavior where vertices always travel toward their preferable neighboring groups. An optimal modular structure can emerge from a collection of these active nodes during a self-organization process where vertices constantly regroup. In addition, we show that our algorithm appears advantageous over other competing methods (e.g., the Newman-fast algorithm) through intensive evaluation. The applications in three real-world networks demonstrate the superiority of our algorithm to find communities that are parallel with the appropriate organization in reality. PMID:18999501

  2. Initial condition for efficient mapping of level set algorithms on many-core architectures

    NASA Astrophysics Data System (ADS)

    Tornai, Gábor János; Cserey, György

    2014-12-01

    In this paper, we investigated the effect of adding more small curves to the initial condition which determines the required number of iterations of a fast level set (LS) evolution. As a result, we discovered two new theorems and developed a proof on the worst case of the required number of iterations. Furthermore, we found that these kinds of initial conditions fit well to many-core architectures. To show this, we have included two case studies which are presented on different platforms. One runs on a graphical processing unit (GPU) and the other is executed on a cellular nonlinear network universal machine (CNN-UM). With the new initial conditions, the steady-state solutions of the LS are reached in less than eight iterations depending on the granularity of the initial condition. These dense iterations can be calculated very quickly on many-core platforms according to the two case studies. In the case of the proposed dense initial condition on GPU, there is a significant speedup compared to the sparse initial condition in all cases since our dense initial condition together with the algorithm utilizes the properties of the underlying architecture. Therefore, greater performance gain can be achieved (up to 18 times speedup compared to the sparse initial condition on GPU). Additionally, we have validated our concept against numerically approximated LS evolution of standard flows (mean curvature, Chan-Vese, geodesic active regions). The dice indexes between the fast LS evolutions and the evolutions of the numerically approximated partial differential equations are in the range of 0.99±0.003.

  3. Algorithmic and architectural optimizations for computationally efficient particle filtering.

    PubMed

    Sankaranarayanan, Aswin C; Srivastava, Ankur; Chellappa, Rama

    2008-05-01

    In this paper, we analyze the computational challenges in implementing particle filtering, especially to video sequences. Particle filtering is a technique used for filtering nonlinear dynamical systems driven by non-Gaussian noise processes. It has found widespread applications in detection, navigation, and tracking problems. Although, in general, particle filtering methods yield improved results, it is difficult to achieve real time performance. In this paper, we analyze the computational drawbacks of traditional particle filtering algorithms, and present a method for implementing the particle filter using the Independent Metropolis Hastings sampler, that is highly amenable to pipelined implementations and parallelization. We analyze the implementations of the proposed algorithm, and, in particular, concentrate on implementations that have minimum processing times. It is shown that the design parameters for the fastest implementation can be chosen by solving a set of convex programs. The proposed computational methodology was verified using a cluster of PCs for the application of visual tracking. We demonstrate a linear speed-up of the algorithm using the methodology proposed in the paper. PMID:18390378

  4. Algorithm for parametric community detection in networks.

    PubMed

    Bettinelli, Andrea; Hansen, Pierre; Liberti, Leo

    2012-07-01

    Modularity maximization is extensively used to detect communities in complex networks. It has been shown, however, that this method suffers from a resolution limit: Small communities may be undetectable in the presence of larger ones even if they are very dense. To alleviate this defect, various modifications of the modularity function have been proposed as well as multiresolution methods. In this paper we systematically study a simple model (proposed by Pons and Latapy [Theor. Comput. Sci. 412, 892 (2011)] and similar to the parametric model of Reichardt and Bornholdt [Phys. Rev. E 74, 016110 (2006)]) with a single parameter α that balances the fraction of within community edges and the expected fraction of edges according to the configuration model. An exact algorithm is proposed to find optimal solutions for all values of α as well as the corresponding successive intervals of α values for which they are optimal. This algorithm relies upon a routine for exact modularity maximization and is limited to moderate size instances. An agglomerative hierarchical heuristic is therefore proposed to address parametric modularity detection in large networks. At each iteration the smallest value of α for which it is worthwhile to merge two communities of the current partition is found. Then merging is performed and the data are updated accordingly. An implementation is proposed with the same time and space complexity as the well-known Clauset-Newman-Moore (CNM) heuristic [Phys. Rev. E 70, 066111 (2004)]. Experimental results on artificial and real world problems show that (i) communities are detected by both exact and heuristic methods for all values of the parameter α; (ii) the dendrogram summarizing the results of the heuristic method provides a useful tool for substantive analysis, as illustrated particularly on a Les Misérables data set; (iii) the difference between the parametric modularity values given by the exact method and those given by the heuristic is

  5. A fast algorithm for parallel computation of multibody dynamics on MIMD parallel architectures

    NASA Technical Reports Server (NTRS)

    Fijany, Amir; Kwan, Gregory; Bagherzadeh, Nader

    1993-01-01

    In this paper the implementation of a parallel O(LogN) algorithm for computation of rigid multibody dynamics on a Hypercube MIMD parallel architecture is presented. To our knowledge, this is the first algorithm that achieves the time lower bound of O(LogN) by using an optimal number of O(N) processors. However, in addition to its theoretical significance, the algorithm is also highly efficient for practical implementation on commercially available MIMD parallel architectures due to its highly coarse grain size and simple communication and synchronization requirements. We present a multilevel parallel computation strategy for implementation of the algorithm on a Hypercube. This strategy allows the exploitation of parallelism at several computational levels as well as maximum overlapping of computation and communication to increase the performance of parallel computation.

  6. ScaleNet--multiscale neural-network architecture for time series prediction.

    PubMed

    Geva, A B

    1998-01-01

    The effectiveness of a multiscale neural-network (NN) architecture for the time series prediction of nonlinear dynamic systems has been investigated. The prediction task is simplified by decomposing different scales of past windows into different scales of wavelets (local frequencies), and predicting the coefficients of each scale of wavelets by means of a separate multilayer perceptron NN. The short-term history (short past windows) is decomposed into the lower scales of wavelet coefficients (high frequencies) which are utilized for "detailed" analysis and prediction, while the long-term history (long past window) is decomposed into higher scales of wavelet coefficients (low frequencies) that are used for the analysis and prediction of slow trends in the time series. These coordinated scales of time and frequency provides an interpretation of the series structures, and more information about the history of the series, using fewer coefficients than other methods. The prediction's results concerning all the different scales of time and frequencies are combined by another "expert" perceptron NN which learns the weight of each scale in the goal-prediction of the original time series. Each network is trained by the backpropagation algorithm using the Levenberg-Marquadt method. The weights and biases are initialized by a new clustering algorithm of the temporal patterns of the time series, which improves the prediction results as compared to random initialization. Three main sets of data were analyzed: the sunspots' benchmark, fluctuations in a farinfrared laser and a nonlinear numerically generated series. Taking the ultimate goal to be the accuracy of the prediction, we found that the suggested multiscale architecture outperforms the corresponding single-scale architectures. The employment of improved learning methods for each of the ScaleNet networks can further improve the prediction results. PMID:18255824

  7. Communication-efficient parallel architectures and algorithms for image computations

    SciTech Connect

    Alnuweiri, H.M.

    1989-01-01

    The main purpose of this dissertation is the design of efficient parallel techniques for image computations which require global operations on image pixels, as well as the development of parallel architectures with special communication features which can support global data movement efficiently. The class of image problems considered in this dissertation involves global operations on image pixels, and irregular (data-dependent) data movement operations. Such problems include histogramming, component labeling, proximity computations, computing the Hough Transform, computing convexity of regions and related properties such as computing the diameter and a smallest area enclosing rectangle for each region. Images with multiple figures and multiple labeled-sets of pixels are also considered. Efficient solutions to such problems involve integer sorting, graph theoretic techniques, and techniques from computational geometry. Although such solutions are not computationally intensive (they all require O(n{sup 2}) operations to be performed on an n {times} n image), they require global communications. The emphasis here is on developing parallel techniques for data movement, reduction, and distribution, which lead to processor-time optimal solutions for such problems on the proposed organizations. The proposed parallel architectures are based on a memory array which can be viewed as an arrangement of memory modules in a k-dimensional space such that the modules are connected to buses placed parallel to the orthogonal axes of the space, and each bus is connected to one processor or a group of processors. It will be shown that such organizations are communication-efficient and are thus highly suited to the image problems considered here, and also to several other classes of problems. The proposed organizations have p processors and O(n{sup 2}) words of memory to process n {times} n images.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Using Distributed Sensor Network Architecture to Link Heterogeneous Astronomical Assets

    NASA Astrophysics Data System (ADS)

    White, R.; Evans, S.; Pergande, J.; Vestrand, W.; Wozniak, P.; Wren, J.

    The internet has brought about great change in the astronomical community, but this interconnectivity is just starting to be exploited for use in this type of instrumentation. Here we present the Telescope ALert Operations Network System (TALONS), a network software suite that allows intercommunication between external and internal astronomical resources and controls the distribution of information to each of the resources. TALONS is an fundamental element of the Thinking Telescopes System, in operation at Los Alamos National Laboratory, and has been enabling great science for the past four years. The system allows a distributed network of telescopes to perform more efficiently in synchronous operation than as individual instruments. TALONS is designed as a merger between a standard server/client architecture and a Distributed Sensor Network (DSN). It can dynamically regulate its client base, allowing any number of heterogeneous resources to be linked together and communicate. TALONS couples that capability with collaborative analysis and maintenance modules so that it can respond quickly to external requests and changing network environments. TALONS clients connect via an intelligent agent, which acts in proxy for the scientist, allowing the telescope to analyze incoming information and respond autonomously. TALONS has a proven track record of effectively supporting the instruments at Los Alamos and other astronomical resources around the world.

  13. Optimization algorithms for new computer architectures with applications to personnel-assignment models. Annual report (Final), 1 May 1988-30 April 1989

    SciTech Connect

    Kennington, J.L.; Helgason, R.V.

    1989-06-29

    One of the most important computer architecture innovations to appear in the marketplace during the last ten years is parallel processing on a shared memory multicomputer. This report presents empirical results on a Sequent Symmetry S81 on four optimization models used in the area of personnel assignment. Both the detailed algorithms and computational results are presented. Contents: Minimal Spanning Trees - An Empirical Investigation of Parallel Algorithms; Dijkstra's Two-tree Shortest Path Algorithm; An Empirical Analysis of the Dense Assignment Problem; and Solving Generalized Network Problems on a Shared Memory Multiprocessor.

  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. Mapping of neural networks onto the memory-processor integrated architecture.

    PubMed

    Kim, Youngsik; Noh, Mi Jung; Han, Tack Don; Kim, Shin Dug

    1998-08-01

    In this paper, an effective memory-processor integrated architecture, called memory-based processor array for artificial neural networks (MPAA), is proposed. The MPAA can be easily integrated into any host system via memory interface. Specifically, the MPA system provides an efficient mechanism for its local memory accesses allowed by row and column bases, using hybrid row and column decoding, which is suitable for computation models of ANNs such as the accessing and alignment patterns given for matrix-by-vector operations. Mapping algorithms to implement the multilayer perceptron with backpropagation learning on the MPAA system are also provided. The proposed algorithms support both neuron and layer level parallelisms which allow the MPAA system to operate the learning phase as well as the recall phase in the pipelined fashion. Performance evaluation is provided by detailed comparison in terms of two metrics such as the cost and number of computation steps. The results show that the performance of the proposed architecture and algorithms is superior to those of the previous approaches, such as one-dimensional single-instruction multiple data (SIMD) arrays, two-dimensional SIMD arrays, systolic ring structures, and hypercube machines. PMID:12662777

  16. 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.

  17. 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.

  18. 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

  19. Algorithm architecture co-design for ultra low-power image sensor

    NASA Astrophysics Data System (ADS)

    Laforest, T.; Dupret, A.; Verdant, A.; Lattard, D.; Villard, P.

    2012-03-01

    In a context of embedded video surveillance, stand alone leftbehind image sensors are used to detect events with high level of confidence, but also with a very low power consumption. Using a steady camera, motion detection algorithms based on background estimation to find regions in movement are simple to implement and computationally efficient. To reduce power consumption, the background is estimated using a down sampled image formed of macropixels. In order to extend the class of moving objects to be detected, we propose an original mixed mode architecture developed thanks to an algorithm architecture co-design methodology. This programmable architecture is composed of a vector of SIMD processors. A basic RISC architecture was optimized in order to implement motion detection algorithms with a dedicated set of 42 instructions. Definition of delta modulation as a calculation primitive has allowed to implement algorithms in a very compact way. Thereby, a 1920x1080@25fps CMOS image sensor performing integrated motion detection is proposed with a power estimation of 1.8 mW.

  20. 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.

  1. A Systolic Array-Based FPGA Parallel Architecture for the BLAST Algorithm

    PubMed Central

    Guo, Xinyu; Wang, Hong; Devabhaktuni, Vijay

    2012-01-01

    A design of systolic array-based Field Programmable Gate Array (FPGA) parallel architecture for Basic Local Alignment Search Tool (BLAST) Algorithm is proposed. BLAST is a heuristic biological sequence alignment algorithm which has been used by bioinformatics experts. In contrast to other designs that detect at most one hit in one-clock-cycle, our design applies a Multiple Hits Detection Module which is a pipelining systolic array to search multiple hits in a single-clock-cycle. Further, we designed a Hits Combination Block which combines overlapping hits from systolic array into one hit. These implementations completed the first and second step of BLAST architecture and achieved significant speedup comparing with previously published architectures. PMID:25969747

  2. Self-contained algorithms to detect communities in networks

    NASA Astrophysics Data System (ADS)

    Castellano, C.; Cecconi, F.; Loreto, V.; Parisi, D.; Radicchi, F.

    2004-03-01

    The investigation of community structures in networks is an important issue in many domains and disciplines. In this paper we present a new class of local and fast algorithms which incorporate a quantitative definition of community. In this way the algorithms for the identification of the community structure become fully self-contained and one does not need additional non-topological information in order to evaluate the accuracy of the results. The new algorithms are tested on artificial and real-world graphs. In particular we show how the new algorithms apply to a network of scientific collaborations both in the unweighted and in the weighted version. Moreover we discuss the applicability of these algorithms to other non-social networks and we present preliminary results about the detection of community structures in networks of interacting proteins.

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

    PubMed Central

    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. PMID:22268218

  4. 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

  5. Learning evasive maneuvers using evolutionary algorithms and neural networks

    NASA Astrophysics Data System (ADS)

    Kang, Moung Hung

    In this research, evolutionary algorithms and recurrent neural networks are combined to evolve control knowledge to help pilots avoid being struck by a missile, based on a two-dimensional air combat simulation model. The recurrent neural network is used for representing the pilot's control knowledge and evolutionary algorithms (i.e., Genetic Algorithms, Evolution Strategies, and Evolutionary Programming) are used for optimizing the weights and/or topology of the recurrent neural network. The simulation model of the two-dimensional evasive maneuver problem evolved is used for evaluating the performance of the recurrent neural network. Five typical air combat conditions were selected to evaluate the performance of the recurrent neural networks evolved by the evolutionary algorithms. Analysis of Variance (ANOVA) tests and response graphs were used to analyze the results. Overall, there was little difference in the performance of the three evolutionary algorithms used to evolve the control knowledge. However, the number of generations of each algorithm required to obtain the best performance was significantly different. ES converges the fastest, followed by EP and then by GA. The recurrent neural networks evolved by the evolutionary algorithms provided better performance than the traditional recommendations for evasive maneuvers, maximum gravitational turn, for each air combat condition. Furthermore, the recommended actions of the recurrent neural networks are reasonable and can be used for pilot training.

  6. 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.

  7. Genetic-algorithm-based tri-state neural networks

    NASA Astrophysics Data System (ADS)

    Uang, Chii-Maw; Chen, Wen-Gong; Horng, Ji-Bin

    2002-09-01

    A new method, using genetic algorithms, for constructing a tri-state neural network is presented. The global searching features of the genetic algorithms are adopted to help us easily find the interconnection weight matrix of a bipolar neural network. The construction method is based on the biological nervous systems, which evolve the parameters encoded in genes. Taking the advantages of conventional (binary) genetic algorithms, a two-level chromosome structure is proposed for training the tri-state neural network. A Matlab program is developed for simulating the network performances. The results show that the proposed genetic algorithms method not only has the features of accurate of constructing the interconnection weight matrix, but also has better network performance.

  8. Projection-based geometrical feature extraction for computer vision: Algorithms in pipeline architectures

    SciTech Connect

    Sanz, J.L.; Dinstein, I.

    1987-01-01

    In this paper, some image transforms and features such as projections along linear patterns, convex hull approximations, Hough transform for line detection, diameter, moments, and principal components will be considered. Specifically, we present algorithms for computing these features which are suitable for implementation in image analysis pipeline architectures. In particular, random access memories and other dedicated hardware components which may be found in the implementation of classical techniques are not longer needed in our algorithms. The effectiveness of our approach is demonstrated by running some of the new algorithms in conventional short-pipelines for image analysis.

  9. An Efficient VLSI Architecture for Multi-Channel Spike Sorting Using a Generalized Hebbian Algorithm

    PubMed Central

    Chen, Ying-Lun; Hwang, Wen-Jyi; Ke, Chi-En

    2015-01-01

    A novel VLSI architecture for multi-channel online spike sorting is presented in this paper. In the architecture, the spike detection is based on nonlinear energy operator (NEO), and the feature extraction is carried out by the generalized Hebbian algorithm (GHA). To lower the power consumption and area costs of the circuits, all of the channels share the same core for spike detection and feature extraction operations. Each channel has dedicated buffers for storing the detected spikes and the principal components of that channel. The proposed circuit also contains a clock gating system supplying the clock to only the buffers of channels currently using the computation core to further reduce the power consumption. The architecture has been implemented by an application-specific integrated circuit (ASIC) with 90-nm technology. Comparisons to the existing works show that the proposed architecture has lower power consumption and hardware area costs for real-time multi-channel spike detection and feature extraction. PMID:26287193

  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. Parallel implementation of the genetic algorithm on NVIDIA GPU architecture for synthesis and inversion

    NASA Astrophysics Data System (ADS)

    Karthik, Victor U.; Sivasuthan, Sivamayam; Hoole, Samuel Ratnajeevan H.

    2014-02-01

    The computational algorithms for device synthesis and nondestructive evaluation (NDE) are often the same. In both we have a goal - a particular field configuration yielding the design performance in synthesis or to match exterior measurements in NDE. The geometry of the design or the postulated interior defect is then computed. Several optimization methods are available for this. The most efficient like conjugate gradients are very complex to program for the required derivative information. The least efficient zeroth order algorithms like the genetic algorithm take much computational time but little programming effort. This paper reports launching a Genetic Algorithm kernel on thousands of compute unified device architecture (CUDA) threads exploiting the NVIDIA graphics processing unit (GPU) architecture. The efficiency of parallelization, although below that on shared memory supercomputer architectures, is quite effective in cutting down solution time into the realm of the practicable. We carry this further into multi-physics electro-heat problems where the parameters of description are in the electrical problem and the object function in the thermal problem. Indeed, this is where the derivative of the object function in the heat problem with respect to the parameters in the electrical problem is the most difficult to compute for gradient methods, and where the genetic algorithm is most easily implemented.

  12. Efficient algorithm to compute mutually connected components in interdependent networks.

    PubMed

    Hwang, S; Choi, S; Lee, Deokjae; Kahng, B

    2015-02-01

    Mutually connected components (MCCs) play an important role as a measure of resilience in the study of interdependent networks. Despite their importance, an efficient algorithm to obtain the statistics of all MCCs during the removal of links has thus far been absent. Here, using a well-known fully dynamic graph algorithm, we propose an efficient algorithm to accomplish this task. We show that the time complexity of this algorithm is approximately O(N(1.2)) for random graphs, which is more efficient than O(N(2)) of the brute-force algorithm. We confirm the correctness of our algorithm by comparing the behavior of the order parameter as links are removed with existing results for three types of double-layer multiplex networks. We anticipate that this algorithm will be used for simulations of large-size systems that have been previously inaccessible. PMID:25768559

  13. Improved ZigBee Network Routing Algorithm Based on LEACH

    NASA Astrophysics Data System (ADS)

    Zhao, Yawei; Zhang, Guohua; Xia, Zhongwu; Li, Xinhua

    Energy efficiency design of routing protocol is a kind of the key technologies used to wireless sensor networks. The paper introduces the ZigBee technology, summarizes the current transmitting routing model in wireless sensor networks, and finds that the traditional LEACH protocol can lead to overload of some cluster head nodes. The paper suggested that the existing LEACH agreement was improved and the new algorithm was better than traditional LEACH routing algorithm by the comprasion of simulation. The improved routing algorithm can prolong the networks lifetime and effectively save the scarce energy.

  14. Genetic Algorithm Based Neural Networks for Nonlinear Optimization

    1994-09-28

    This software develops a novel approach to nonlinear optimization using genetic algorithm based neural networks. To our best knowledge, this approach represents the first attempt at applying both neural network and genetic algorithm techniques to solve a nonlinear optimization problem. The approach constructs a neural network structure and an appropriately shaped energy surface whose minima correspond to optimal solutions of the problem. A genetic algorithm is employed to perform a parallel and powerful search ofmore » the energy surface.« less

  15. 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.

  16. An Adaptive Hybrid Algorithm for Global Network Alignment.

    PubMed

    Xie, Jiang; Xiang, Chaojuan; Ma, Jin; Tan, Jun; Wen, Tieqiao; Lei, Jinzhi; Nie, Qing

    2016-01-01

    It is challenging to obtain reliable and optimal mapping between networks for alignment algorithms when both nodal and topological structures are taken into consideration due to the underlying NP-hard problem. Here, we introduce an adaptive hybrid algorithm that combines the classical Hungarian algorithm and the Greedy algorithm (HGA) for the global alignment of biomolecular networks. With this hybrid algorithm, every pair of nodes with one in each network is first aligned based on node information (e.g., their sequence attributes) and then followed by an adaptive and convergent iteration procedure for aligning the topological connections in the networks. For four well-studied protein interaction networks, i.e., C.elegans, yeast, D.melanogaster, and human, applications of HGA lead to improved alignments in acceptable running time. The mapping between yeast and human PINs obtained by the new algorithm has the largest value of common gene ontology (GO) terms compared to those obtained by other existing algorithms, while it still has lower Mean normalized entropy (MNE) and good performances on several other measures. Overall, the adaptive HGA is effective and capable of providing good mappings between aligned networks in which the biological properties of both the nodes and the connections are important. PMID:27295633

  17. An algorithm for three-dimensional Voronoi S-network.

    PubMed

    Medvedev, N N; Voloshin, V P; Luchnikov, V A; Gavrilova, M L

    2006-11-15

    The paper presents an algorithm for calculating the three-dimensional Voronoi-Delaunay tessellation for an ensemble of spheres of different radii (additively-weighted Voronoi diagram). Data structure and output of the algorithm is oriented toward the exploration of the voids between the spheres. The main geometric construct that we develop is the Voronoi S-network (the network of vertices and edges of the Voronoi regions determined in relation to the surfaces of the spheres). General scheme of the algorithm and the key points of its realization are discussed. The principle of the algorithm is that for each determined site of the network we find its neighbor sites. Thus, starting from a known site of the network, we sequentially find the whole network. The starting site of the network is easily determined based on certain considerations. Geometric properties of ensembles of spheres of different radii are discussed, the conditions of applicability and limitations of the algorithm are indicated. The algorithm is capable of working with a wide variety of physical models, which may be represented as sets of spheres, including computer models of complex molecular systems. Emphasis was placed on the issue of increasing the efficiency of algorithm to work with large models (tens of thousands of atoms). It was demonstrated that the experimental CPU time increases linearly with the number of atoms in the system, O(n). PMID:16900490

  18. 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. PMID:26173905

  19. 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.

  20. Advanced Algorithms for Local Routing Strategy on Complex Networks.

    PubMed

    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

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. AES-128 Bit Algorithm Using Fully Pipelined Architecture for Secret Communication

    NASA Astrophysics Data System (ADS)

    Gnanambika, M.; Adilakshmi, S.; Noorbasha, Fazal

    2012-03-01

    In this paper, an efficient method for high speed hardware implementation of AES algorithm is presented. So far, many implementations of AES have been proposed, for various goals that effect the Sub Byte transformation in various ways. These methods of implementation are based on combinational logic and are done in polynomial bases. In the proposed architecture, it is done by using composite field arithmetic in normal bases. In addition, efficient key expansion architecture suitable for 6 sub pipelined round units is also presented. These designs were described using VerilogHDL, simulated using Modelsim.

  6. Mutual Algorithm-Architecture Analysis for Real - Parallel Systems in Particle Physics Experiments.

    NASA Astrophysics Data System (ADS)

    Ni, Ping

    Data acquisition from particle colliders requires real-time detection of tracks and energy clusters from collision events occurring at intervals of tens of mus. Beginning with the specification of a benchmark track-finding algorithm, parallel implementations have been developed. A revision of the routing scheme for performing reductions such as a tree sum, called the reduced routing distance scheme, has been developed and analyzed. The scheme reduces inter-PE communication time for narrow communication channel systems. A new parallel algorithm, called the interleaved tree sum, for parallel reduction problems has been developed that increases efficiency of processor use. Detailed analysis of this algorithm with different routing schemes is presented. Comparable parallel algorithms are analyzed, also taking into account the architectural parameters that play an important role in this parallel algorithm analysis. Computation and communication times are analyzed to guide the design of a custom system based on a massively parallel processing component. Developing an optimal system requires mutual analysis of algorithm and architecture parameters. It is shown that matching a processor array size to the parallelism of the problem does not always produce the best system design. Based on promising benchmark simulation results, an application specific hardware prototype board, called Dasher, has been built using two Blitzen chips. The processing array is a mesh-connected SIMD system with 256 PEs. Its design is discussed, with details on the software environment.

  7. 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.

  8. 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.

  9. Establishing the Architecture of Plant Gene Regulatory Networks.

    PubMed

    Yang, F; Ouma, W Z; Li, W; Doseff, A I; Grotewold, E

    2016-01-01

    Gene regulatory grids (GRGs) encompass the space of all the possible transcription factor (TF)-target gene interactions that regulate gene expression, with gene regulatory networks (GRNs) representing a temporal and spatial manifestation of a portion of the GRG, essential for the specification of gene expression. Thus, understanding GRG architecture provides a valuable tool to explain how genes are expressed in an organism, an important aspect of synthetic biology and essential toward the development of the "in silico" cell. Progress has been made in some unicellular model systems (eg, yeast), but significant challenges remain in more complex multicellular organisms such as plants. Key to understanding the organization of GRGs is therefore identifying the genes that TFs bind to, and control. The application of sensitive and high-throughput methods to investigate genome-wide TF-target gene interactions is providing a wealth of information that can be linked to important agronomic traits. We describe here the methods and resources that have been developed to investigate the architecture of plant GRGs and GRNs. We also provide information regarding where to obtain clones or other resources necessary for synthetic biology or metabolic engineering. PMID:27480690

  10. An architecture for transmit beamforming for rapidly deployable radio networks

    SciTech Connect

    Prescott, G.E.; Sparks, C.A.; Sivaprakasam, S.

    1997-01-01

    Beamforming Technology will be an essential element in tactical battlefield communication systems of the next generation. Only with beamforming will the demands of communication quality, network access and covertness be jointly achieved. This paper focuses on the signal processing features of one element of this technology-transmitter beamforming. A flexible beamforming architecture such as the one described here will facilitate ongoing research into the development of a high speed ATM-based wireless communication system currently being investigated at the University of Kansas. This system provides for spatial frequency reuse by allowing multiple transmit beams to be steered to mobile end users. The modulation and the steering angles of the beams adapt under software control in response to the demands of the communications environment and the user{close_quote}s {ital requirements}. {copyright} {ital 1997 American Institute of Physics.}

  11. 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. PMID:27322505

  12. An algorithm for studying rigidity in disordered 3D networks

    NASA Astrophysics Data System (ADS)

    Chubynsky, M. V.; Thorpe, M. F.

    2004-03-01

    Some physical systems, such as covalent glasses and proteins, can be modeled as elastic networks, by dividing the interactions between particles into strong and weak, representing the former as constraints and neglecting the latter. For low enough connectivities, motions maintaining the constraints and thus having zero energy cost are possible. The goal of rigidity analysis is finding the number of such zero energy modes, the rigid clusters and flexible joints between them, as well as stressed bonds. For a certain class of networks there is a very fast graph-theoretical algorithm (the Pebble Game) for doing this analysis, but for more general networks, there are known counterexamples. While generalizing the Pebble Game is the ultimate goal, we propose a slower algorithm capable of doing all the same analyses as the Pebble Game but applicable to any networks. We discuss the applications of this algorithm to specific examples of 3D networks, such as diluted central force lattices, colloidal glasses and proteins.

  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

    2015-01-01

    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

  14. 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

  15. Research on secure routing algorithm in wireless sensor network

    NASA Astrophysics Data System (ADS)

    Zhang, Bo

    2013-03-01

    Through the research on the existing wireless sensor network (WSN) and its security technologies, this paper presents a design of the WSN-based secure routing algorithm. This design uses the existing routing algorithm as chief source, adding the security guidance strategy, introducing the location key information, to enhance the security performance of WSN routing. The improved routing algorithm makes the WSN routing achieve better anti-attack in the case of little overhead increase, therefore has high practical value.

  16. Research of Ad Hoc Networks Access Algorithm

    NASA Astrophysics Data System (ADS)

    Xiang, Ma

    With the continuous development of mobile communication technology, Ad Hoc access network has become a hot research, Ad Hoc access network nodes can be used to expand capacity of multi-hop communication range of mobile communication system, even business adjacent to the community, improve edge data rates. When the ad hoc network is the access network of the internet, the gateway discovery protocol is very important to choose the most appropriate gateway to guarantee the connectivity between ad hoc network and IP based fixed networks. The paper proposes a QoS gateway discovery protocol which uses the time delay and stable route to the gateway selection conditions. And according to the gateway discovery protocol, it also proposes a fast handover scheme which can decrease the handover time and improve the handover efficiency.

  17. 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.

  18. Efficient implementation of Jacobi algorithms and Jacobi sets on distributed memory architectures

    SciTech Connect

    Eberlein, P.J. ); Park, H. )

    1990-04-01

    One-sided methods for implementing Jacobi diagonalization algorithms have been recently proposed for both distributed memory and vector machines. These methods are naturally well suited to distributed memory and vector architectures because of their inherent parallelism and their abundance of vector operations. Also, one-sided methods require substantially less message passing than the two-sided methods, and thus can achieve higher efficiency. The authors describe in detail the use of the one-sided Jacobi rotation as opposed to the rotation used in the Hestenes algorithm; they perceive the difference to have been widely misunderstood. Furthermore, the one-sided algorithm generalizes to other problems such as the nonsymmetric eigenvalue problem while the Hestenes algorithm does not. The authors discuss two new implementations for Jacobi sets for a ring connected array of processors and show their isomorphism to the round-robin ordering.

  19. 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)

  20. 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

  1. 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.

  2. State-dependent architecture of thalamic reticular sub-networks

    PubMed Central

    Halassa, Michael M.; Chen, Zhe; Wimmer, Ralf D.; Brunetti, Philip M.; Zhao, Shengli; Zikopoulos, Basilis; Wang, Fan; Brown, Emery N.; Wilson, Matthew A.

    2014-01-01

    Behavioral state is known to influence interactions between thalamus and cortex, which are important for sensation, action and cognition. The thalamic reticular nucleus (TRN) is hypothesized to regulate thalamo-cortical transmission, but the underlying functional architecture of this process and its state-dependence are unknown. By combining the first TRN ensemble recording with psychophysics and connectivity-based optogenetic tagging, we find that the TRN is composed of distinct sub-networks. While activity of limbic-projecting TRN neurons correlates with arousal, sensory-projecting neurons participate in spindles and show elevated synchrony by slow waves during sleep. Conversely, sensory-projecting neurons are suppressed by attentional states, demonstrating common microcircuit mechanisms of sensory processing in sleep and attention. Bidirectional manipulation of attentional performance was achieved through optogenetic manipulation of these TRN sub-networks. Our findings provide evidence for differential regulation of thalamic inhibition across brain states, suggesting that the TRN separately controls external stimulus processing and internally-generated computations, a basic determinant of cognitive function. PMID:25126786

  3. 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.

  4. 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.

  5. 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.

  6. Cascade Error Projection: A New Learning Algorithm

    NASA Technical Reports Server (NTRS)

    Duong, T. A.; Stubberud, A. R.; Daud, T.; Thakoor, A. P.

    1995-01-01

    A new neural network architecture and a hardware implementable learning algorithm is proposed. The algorithm, called cascade error projection (CEP), handles lack of precision and circuit noise better than existing algorithms.

  7. QuateXelero: an accelerated exact network motif detection algorithm.

    PubMed

    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. Mapping spatio-temporal filtering algorithms used in fluoroscopy to single core and multicore DSP architectures

    NASA Astrophysics Data System (ADS)

    Dasgupta, Udayan; Ali, Murtaza

    2011-03-01

    Low dose X-ray image sequences, as obtained in fluoroscopy, exhibit high levels of noise that must be suppressed in real-time, while preserving diagnostic structures. Multi-step adaptive filtering approaches, often involving spatio-temporal filters, are typically used to achieve this goal. In this work typical fluoroscopic image sequences, corrupted with Poisson noise, were processed using various filtering schemes. The noise suppression of the schemes was evaluated using objective image quality measures. Two adaptive spatio-temporal schemes, the first one using object detection and the second one using unsharp masking, were chosen as representative approaches for different fluoroscopy procedures and mapped on to Texas Instrument's (TI) high performance digital signal processors (DSP). The paper explains the fixed point design of these algorithms and evaluates its impact on overall system performance. The fixed point versions of these algorithms are mapped onto the C64x+TM core using instruction-level parallelism to effectively use its VLIW architecture. The overall data flow was carefully planned to reduce cache and data movement overhead, while working with large medical data sets. Apart from mapping these algorithms on to TI's single core DSP architecture, this work also distributes the operations to leverage multi-core DSP architectures. The data arrangement and flow were optimized to minimize inter-processor messaging and data movement overhead.

  9. 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.

  10. A training algorithm for binary feedforward neural networks.

    PubMed

    Gray, D L; Michel, A N

    1992-01-01

    The authors present a new training algorithm to be used on a four-layer perceptron-type feedforward neural network for the generation of binary-to-binary mappings. This algorithm is called the Boolean-like training algorithm (BLTA) and is derived from original principles of Boolean algebra followed by selected extensions. The algorithm can be implemented on analog hardware, using a four-layer binary feedforward neural network (BFNN). The BLTA does not constitute a traditional circuit building technique. Indeed, the rules which govern the BLTA allow for generalization of data in the face of incompletely specified Boolean functions. When compared with techniques which employ descent methods, training times are greatly reduced in the case of the BLTA. Also, when the BFNN is used in conjunction with A/D converters, the applicability of the present algorithm can be extended to accept real-valued inputs. PMID:18276419

  11. 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.

  12. Agent routing algorithm in wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Zhang, Yuqing; Yang, Shuqun

    2013-03-01

    Wireless sensor networks are a new technology of information acquisition and processing, so they are widely used in all kinds of fields. In the paper we introduce Agent technology into the wireless sensor network, conduct a in-depth research on the four routing schemes, and propose a new improved routing scheme, which considers the energy consumption of both nodes and path. Furthermore, The scheme we proposed has efficient routing function, can balance the energy consumption of nodes and extends the lifetime of the network in a more efficient way.

  13. 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.

  14. 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.

  15. Optimizing the controllability of arbitrary networks with genetic algorithm

    NASA Astrophysics Data System (ADS)

    Li, Xin-Feng; Lu, Zhe-Ming

    2016-04-01

    Recently, as the controllability of complex networks attracts much attention, how to optimize networks' controllability has become a common and urgent problem. In this paper, we develop an efficient genetic algorithm oriented optimization tool to optimize the controllability of arbitrary networks consisting of both state nodes and control nodes under Popov-Belevitch-Hautus rank condition. The experimental results on a number of benchmark networks show the effectiveness of this method and the evolution of network topology is captured. Furthermore, we explore how network structure affects its controllability and find that the sparser a network is, the more control nodes are needed to control it and the larger the differences between node degrees, the more control nodes are needed to achieve the full control. Our framework provides an alternative to controllability optimization and can be applied to arbitrary networks without any limitations.

  16. 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. (©)RSNA, 2016. PMID:26963448

  17. Television Compression Algorithms And Transmission On Packet Networks

    NASA Astrophysics Data System (ADS)

    Brainard, R. C.; Othmer, J. H.

    1988-10-01

    Wide-band packet transmission is a subject of strong current interest. The transmission of compressed TV signals over such networks is possible with any quality level. There are some specific advantages in using packet networks for TV transmission. Namely, any fixed data rate can be chosen, or a variable data rate can be utilized. However, on the negative side packet loss must be considered and differential delay in packet arrival must be compensated. The possibility of packet loss has a strong influence on compression algorithm choice. Differential delay of packet arrival is a new problem in codec design. Some issues relevant to mutual design of the transmission networks and compression algorithms will be presented. An assumption is that the packet network will maintain packet sequence integrity. For variable-rate transmission, a reasonable definition of peak data rate is necessary. Rate constraints may be necessary to encourage instituting a variable-rate service on the networks. The charging algorithm for network use will have an effect on selection of compression algorithm. Some values of and procedures for implementing packet priorities are discussed. Packet length has only a second-order effect on packet-TV considerations. Some examples of a range of codecs for differing data rates and picture quality are given. These serve to illustrate sensitivities to the various characteristics of packet networks. Perhaps more important, we talk about what we do not know about the design of such systems.

  18. Advanced optical network architecture for integrated digital avionics

    NASA Astrophysics Data System (ADS)

    Morgan, D. Reed

    1996-12-01

    For the first time in the history of avionics, the network designer now has a choice in selecting the media that interconnects the sources and sinks of digital data on aircraft. Electrical designs are already giving way to photonics in application areas where the data rate times distance product is large or where special design requirements such as low weight or EMI considerations are critical. Future digital avionic architectures will increasingly favor the use of photonic interconnects as network data rates of one gigabit/second and higher are needed to support real-time operation of high-speed integrated digital processing. As the cost of optical network building blocks is reduced and as temperature-rugged laser sources are matured, metal interconnects will be forced to retreat to applications spanning shorter and shorter distances. Although the trend is already underway, the widespread use of digital optics will first occur at the system level, where gigabit/second, real-time interconnects between sensors, processors, mass memories and displays separated by a least of few meters will be required. The application of photonic interconnects for inter-printed wiring board signalling across the backplane will eventually find application for gigabit/second applications since signal degradation over copper traces occurs before one gigabit/second and 0.5 meters are reached. For the foreseeable future however, metal interconnects will continue to be used to interconnect devices on printed wiring boards since 5 gigabit/second signals can be sent over metal up to around 15 centimeters. Current-day applications of optical interconnects at the system level are described and a projection of how advanced optical interconnect technology will be driven by the use of high speed integrated digital processing on future aircraft is presented. The recommended advanced network for application in the 2010 time frame is a fiber-based system with a signalling speed of around 2

  19. Optimization algorithms for new computer architectures with application to routing and scheduling (Year 3). Final report, 1 May 1989-30 September 1990

    SciTech Connect

    Kennington, J.L.; Helgason, R.V.

    1990-10-01

    One of the most important computer architecture innovations to appear in the market place during the last ten years is parallel processing on a shared memory multicomputer. This report presents new algorithms for a variety of network models along with empirical analysis on both sequential and parallel computers. An empirical study on the AT and T KORBX system is also presented. This system uses eight processors each of which has vector capability. Our research program objective is to develop and empirically test new parallel algorithms and software for a wide variety of optimization problems. The problems studied this past year include the shortest path problem, the assignment problem, the semi-assignment problem, the transportation problem, and the generalized network problem. Algorithms for all of these models have been developed and empirically tested on a variety of computers. In addition, we worked with the Military Airlift Command to test the ATT KORBX system located at Scott Air Force Base.

  20. 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. PMID:24805224

  1. 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.

  2. A stochastic learning algorithm for layered neural networks

    SciTech Connect

    Bartlett, E.B. . Dept. of Mechanical Engineering); Uhrig, R.E. . Dept. of Nuclear Engineering)

    1992-01-01

    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.

  3. 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.

  4. Optimal Design of Geodetic Network Using Genetic Algorithms

    NASA Astrophysics Data System (ADS)

    Vajedian, Sanaz; Bagheri, Hosein

    2010-05-01

    A geodetic network is a network which is measured exactly by techniques of terrestrial surveying based on measurement of angles and distances and can control stability of dams, towers and their around lands and can monitor deformation of surfaces. The main goals of an optimal geodetic network design process include finding proper location of control station (First order Design) as well as proper weight of observations (second order observation) in a way that satisfy all the criteria considered for quality of the network with itself is evaluated by the network's accuracy, reliability (internal and external), sensitivity and cost. The first-order design problem, can be dealt with as a numeric optimization problem. In this designing finding unknown coordinates of network stations is an important issue. For finding these unknown values, network geodetic observations that are angle and distance measurements must be entered in an adjustment method. In this regard, using inverse problem algorithms is needed. Inverse problem algorithms are methods to find optimal solutions for given problems and include classical and evolutionary computations. The classical approaches are analytical methods and are useful in finding the optimum solution of a continuous and differentiable function. Least squares (LS) method is one of the classical techniques that derive estimates for stochastic variables and their distribution parameters from observed samples. The evolutionary algorithms are adaptive procedures of optimization and search that find solutions to problems inspired by the mechanisms of natural evolution. These methods generate new points in the search space by applying operators to current points and statistically moving toward more optimal places in the search space. Genetic algorithm (GA) is an evolutionary algorithm considered in this paper. This algorithm starts with definition of initial population, and then the operators of selection, replication and variation are applied

  5. An events based algorithm for distributing concurrent tasks on multi-core architectures

    NASA Astrophysics Data System (ADS)

    Holmes, David W.; Williams, John R.; Tilke, Peter

    2010-02-01

    In this paper, a programming model is presented which enables scalable parallel performance on multi-core shared memory architectures. The model has been developed for application to a wide range of numerical simulation problems. Such problems involve time stepping or iteration algorithms where synchronization of multiple threads of execution is required. It is shown that traditional approaches to parallelism including message passing and scatter-gather can be improved upon in terms of speed-up and memory management. Using spatial decomposition to create orthogonal computational tasks, a new task management algorithm called H-Dispatch is developed. This algorithm makes efficient use of memory resources by limiting the need for garbage collection and takes optimal advantage of multiple cores by employing a "hungry" pull strategy. The technique is demonstrated on a simple finite difference solver and results are compared to traditional MPI and scatter-gather approaches. The H-Dispatch approach achieves near linear speed-up with results for efficiency of 85% on a 24-core machine. It is noted that the H-Dispatch algorithm is quite general and can be applied to a wide class of computational tasks on heterogeneous architectures involving multi-core and GPGPU hardware.

  6. A new hardware-efficient algorithm and reconfigurable architecture for image contrast enhancement.

    PubMed

    Huang, Shih-Chia; Chen, Wen-Chieh

    2014-10-01

    Contrast enhancement is crucial when generating high quality images for image processing applications, such as digital image or video photography, liquid crystal display processing, and medical image analysis. In order to achieve real-time performance for high-definition video applications, it is necessary to design efficient contrast enhancement hardware architecture to meet the needs of real-time processing. In this paper, we propose a novel hardware-oriented contrast enhancement algorithm which can be implemented effectively for hardware design. In order to be considered for hardware implementation, approximation techniques are proposed to reduce these complex computations during performance of the contrast enhancement algorithm. The proposed hardware-oriented contrast enhancement algorithm achieves good image quality by measuring the results of qualitative and quantitative analyzes. To decrease hardware cost and improve hardware utilization for real-time performance, a reduction in circuit area is proposed through use of parameter-controlled reconfigurable architecture. The experiment results show that the proposed hardware-oriented contrast enhancement algorithm can provide an average frame rate of 48.23 frames/s at high definition resolution 1920 × 1080. PMID:25148665

  7. 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.

  8. Performance evaluation of power control algorithms in wireless cellular networks

    NASA Astrophysics Data System (ADS)

    Temaneh-Nyah, C.; Iita, V.

    2014-10-01

    Power control in a mobile communication network intents to control the transmission power levels in such a way that the required quality of service (QoS) for the users is guaranteed with lowest possible transmission powers. Most of the studies of power control algorithms in the literature are based on some kind of simplified assumptions which leads to compromise in the validity of the results when applied in a real environment. In this paper, a CDMA network was simulated. The real environment was accounted for by defining the analysis area and the network base stations and mobile stations are defined by their geographical coordinates, the mobility of the mobile stations is accounted for. The simulation also allowed for a number of network parameters including the network traffic, and the wireless channel models to be modified. Finally, we present the simulation results of a convergence speed based comparative analysis of three uplink power control algorithms.

  9. 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. PMID:22408514

  10. 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

  11. Applying a service-based architecture to autonomous distributed sensor networks

    NASA Astrophysics Data System (ADS)

    Patrone, David M.; Patrone, Dennis S.; Wenstrand, Doug S.; Smith, Dexter G.

    2004-04-01

    Traditional distributed architectures are not sufficient when developing an autonomous, distributed sensor network. In order to be truly autonomous, a distributed sensor network must be able to survive and reconfigure in-the-field without manual intervention. A limitation of traditional distributed architectures, such as client/server or peer-to-peer, within an autonomous network is that the distributed devices and applications are tightly coupled by their communication protocols prior to implementation and deployment. The introduction of new devices and applications in the field is difficult due to this coupling. Also, autonomous reconfiguration of the devices on the network due to faults or addition of new devices is extremely difficult unless the devices are homogeneous. A service-based architecture is proposed as an alternative architecture for creating autonomous, distributed sensor networks. The service-based approach provides the ability to create a scalable, self-configuring, and self-healing network for building and maintaining large, emerging and ad-hoc virtual networks of devices and applications. New devices can be automatically discovered by current devices on the network and automatically integrated into the system without manual intervention. This paper will explain the benefits and limitations of applying a service-based architecture to autonomous, distributed sensor networks and compare this approach with traditional architectures such as client/server and peer-to-peer. A description will be given of a prototype system developed using service-enabled seismic, acoustic, and visual sensors.

  12. 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.

  13. 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

  14. 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

  15. Potential unsatisfiability of cyclic constraints on stochastic biological networks biases selection towards hierarchical architectures

    PubMed Central

    Smith, Cameron; Pechuan, Ximo; Puzio, Raymond S.; Biro, Daniel; Bergman, Aviv

    2015-01-01

    Constraints placed upon the phenotypes of organisms result from their interactions with the environment. Over evolutionary time scales, these constraints feed back onto smaller molecular subnetworks comprising the organism. The evolution of biological networks is studied by considering a network of a few nodes embedded in a larger context. Taking into account this fact that any network under study is actually embedded in a larger context, we define network architecture, not on the basis of physical interactions alone, but rather as a specification of the manner in which constraints are placed upon the states of its nodes. We show that such network architectures possessing cycles in their topology, in contrast to those that do not, may be subjected to unsatisfiable constraints. This may be a significant factor leading to selection biased against those network architectures where such inconsistent constraints are more likely to arise. We proceed to quantify the likelihood of inconsistency arising as a function of network architecture finding that, in the absence of sampling bias over the space of possible constraints and for a given network size, networks with a larger number of cycles are more likely to have unsatisfiable constraints placed upon them. Our results identify a constraint that, at least in isolation, would contribute to a bias in the evolutionary process towards more hierarchical -modular versus completely connected network architectures. Together, these results highlight the context dependence of the functionality of biological networks. PMID:26040595

  16. 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.

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

    PubMed

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

    2015-01-01

    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

  18. 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

  19. Optical packet switch architectures for ultrahigh-speed networks

    NASA Astrophysics Data System (ADS)

    O'Mahony, M. J.; Klonidis, Dimitris; Politi, Christina; Negabati, Reza; Simeonidou, Dimitra

    2005-11-01

    Optical packet switching is commonly considered as a possible technology for future telecommunication networks, due to its compatibility with bursty traffic, eg Internet protocol (IP), and efficient use of wavelength channels. Current transport networks are voice-optimised and connection oriented, however the amount of data traffic is rapidly increasing, resulting in a continuous increase of average traffic through major exchanges exceeding 30% per annum (in Europe). Thus optical packet switching is seen as a future technology that will support diverse traffic profiles and give more efficient bandwidth utilisation through its ability to provide multiplexing at the packet level. In recent years the significance of optical packet switching as an emerging technology has been identified and researched by a number of research groups. Earlier optical packet switching demonstrators presented switching of mainly ATM compatible synchronously transmitted packets at bit rates up to 2.5b/s with the optical header encoded either in series or in parallel to the payload using the sub-carrier modulation technique. More recent projects have demonstrated switching capabilities at 10Gb/s using more advanced approaches with special encoding schemes for header and header detection, together with sophisticated control mechanisms for contention resolution. The capability of switching optical packets at bit rates up to 160Gb/s has recently been demonstrated. This paper discusses the architectures currently proposed for high speed optical packet switching, including the key techniques of header processing and payload switching. The focus is on a high speed demonstrator [OPSnet] capable of operation at rates >100 Gb/s.

  20. 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.

  1. Analyzing milestoning networks for molecular kinetics: Definitions, algorithms, and examples

    NASA Astrophysics Data System (ADS)

    Viswanath, Shruthi; Kreuzer, Steven M.; Cardenas, Alfredo E.; Elber, Ron

    2013-11-01

    Network representations are becoming increasingly popular for analyzing kinetic data from techniques like Milestoning, Markov State Models, and Transition Path Theory. Mapping continuous phase space trajectories into a relatively small number of discrete states helps in visualization of the data and in dissecting complex dynamics to concrete mechanisms. However, not only are molecular networks derived from molecular dynamics simulations growing in number, they are also getting increasingly complex, owing partly to the growth in computer power that allows us to generate longer and better converged trajectories. The increased complexity of the networks makes simple interpretation and qualitative insight of the molecular systems more difficult to achieve. In this paper, we focus on various network representations of kinetic data and algorithms to identify important edges and pathways in these networks. The kinetic data can be local and partial (such as the value of rate coefficients between states) or an exact solution to kinetic equations for the entire system (such as the stationary flux between vertices). In particular, we focus on the Milestoning method that provides fluxes as the main output. We proposed Global Maximum Weight Pathways as a useful tool for analyzing molecular mechanism in Milestoning networks. A closely related definition was made in the context of Transition Path Theory. We consider three algorithms to find Global Maximum Weight Pathways: Recursive Dijkstra's, Edge-Elimination, and Edge-List Bisection. The asymptotic efficiency of the algorithms is analyzed and numerical tests on finite networks show that Edge-List Bisection and Recursive Dijkstra's algorithms are most efficient for sparse and dense networks, respectively. Pathways are illustrated for two examples: helix unfolding and membrane permeation. Finally, we illustrate that networks based on local kinetic information can lead to incorrect interpretation of molecular mechanisms.

  2. A new adaptive merging and growing algorithm for designing artificial neural networks.

    PubMed

    Islam, Md Monirul; Sattar, Md Abdus; Amin, Md Faijul; Yao, Xin; Murase, Kazuyuki

    2009-06-01

    This paper presents a new algorithm, called adaptive merging and growing algorithm (AMGA), in designing artificial neural networks (ANNs). This algorithm merges and adds hidden neurons during the training process of ANNs. The merge operation introduced in AMGA is a kind of a mixed mode operation, which is equivalent to pruning two neurons and adding one neuron. Unlike most previous studies, AMGA puts emphasis on autonomous functioning in the design process of ANNs. This is the main reason why AMGA uses an adaptive not a predefined fixed strategy in designing ANNs. The adaptive strategy merges or adds hidden neurons based on the learning ability of hidden neurons or the training progress of ANNs. In order to reduce the amount of retraining after modifying ANN architectures, AMGA prunes hidden neurons by merging correlated hidden neurons and adds hidden neurons by splitting existing hidden neurons. The proposed AMGA has been tested on a number of benchmark problems in machine learning and ANNs, including breast cancer, Australian credit card assessment, and diabetes, gene, glass, heart, iris, and thyroid problems. The experimental results show that AMGA can design compact ANN architectures with good generalization ability compared to other algorithms. PMID:19203888

  3. Design And Implementation Of A Multi-Sensor Fusion Algorithm On A Hypercube Computer Architecture

    NASA Astrophysics Data System (ADS)

    Glover, Charles W.

    1990-03-01

    A multi-sensor integration (MSI) algorithm written for sequential single processor computer architecture has been transformed into a concurrent algorithm and implemented in parallel on a multi-processor hypercube computer architecture. This paper will present the philosophy and methodologies used in the decomposition of the sequential MSI algorithm, and its transformation into a parallel MSI algorithm. The parallel MSI algorithm was implemented on a NCUBETM hypercube computer. The performance of the parallel MSI algorithm has been measured and compared against its sequential counterpart by running test case scenarios through a simulation program. The simulation program allows the user to define the trajectories of all players in the scenario, and to pick the sensor suites of the players and their operating characteristics. For example, an air-to-air engagement scenario was used as one of the test cases. In this scenario, two friend aircrafts were being attacked by six foe aircraft in a pincer maneuver. Both the friend and foe aircrafts launch missiles at several different time points in the engagement. The sensor suites on each aircraft are dual mode RADAR, dual mode IRST, and ESM sensors. The modes of the sensors are switched as needed throughout the scenario. The RADAR sensor is used only intermittently, thus most of the MSI information is obtained from passive sensing. The maneuvers in this scenario caused aircraft and missile to constantly fly in and out of sensors field-of-view (F0V). This resulted in the MSI algorithm to constantly reacquire, initiate, and delete new tracks as it tracked all objects in the scenario. The objective was to determine performance of the parallel MSI algorithm in such a complex environment, and to determine how many multi-processors (nodes) of the hypercube could be effectively used by an aircraft in such an environment. For the scenario just discussed, a 4-node hypercube was found to be the optimal size and a factor two in speedup

  4. Using genetic algorithms to construct a network for financial prediction

    NASA Astrophysics Data System (ADS)

    Patel, Devesh

    1996-03-01

    Traditional forecasting models such as the Box-Jenkins ARIMA model are almost all based on models that assume a linear relationship amongst variables and cannot approximate the non- linear relationship that exists amongst variables in real-world data such as stock-price data. Artificial neural networks, on the other hand, consist of two or more levels of nonlinearity that have been successfully used to approximate the underlying relationships of time series data. Neural networks however, pose a design problem: their optimum topology and training rule parameters including learning rate and momentum, for the problem at hand need to be determined. In this paper, we use genetic algorithms to determine these design parameters. In general genetic algorithms are an optimization method that find solutions to a problem by an evolutionary process based on natural selection. The genetic algorithm searches through the network parameter space and the neural network learning algorithm evaluates the selected parameters. We then use the optimally configured network to predict the stock market price of a blue-chip company on the UK market.

  5. 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

  6. Immune allied genetic algorithm for Bayesian network structure learning

    NASA Astrophysics Data System (ADS)

    Song, Qin; Lin, Feng; Sun, Wei; Chang, KC

    2012-06-01

    Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.

  7. 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

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

    PubMed

    Nuwagaba, S; Zhang, F; Hui, C

    2015-05-22

    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

  10. Determination of algorithm parallelism in NP-complete problems for distributed architectures. Master's thesis

    SciTech Connect

    Beard, R.A.

    1990-03-01

    The purpose of this thesis is to explore the methods used to parallelize NP-complete problems and the degree of improvement that can be realized using a distributed parallel processor to solve these combinatoric problems. Common NP-complete problem characteristics such as a priori reductions, use of partial-state information, and inhomogeneous searches are identified and studied. The set covering problem (SCP) is implemented for this research because many applications such as information retrieval, task scheduling, and VLSI expression simplification can be structured as an SCP problem. In addition, its generic NP-complete common characteristics are well documented and a parallel implementation has not been reported. Parallel programming design techniques involve decomposing the problem and developing the parallel algorithms. The major components of a parallel solution are developed in a four phase process. First, a meta-level design is accomplished using an appropriate design language such as UNITY. Then, the UNITY design is transformed into an algorithm and implementation specific to a distributed architecture. Finally, a complexity analysis of the algorithm is performed. the a priori reductions are divided-and-conquer algorithms; whereas, the search for the optimal set cover is accomplished with a branch-and-bound algorithm. The search utilizes a global best cost maintained at a central location for distribution to all processors. Three methods of load balancing are implemented and studied: coarse grain with static allocation of the search space, fine grain with dynamic allocation, and dynamic load balancing.

  11. Efficient implementation of Jacobi algorithms and Jacobi sets on distributed memory architectures

    NASA Astrophysics Data System (ADS)

    Eberlein, P. J.; Park, Haesun

    1990-04-01

    One-sided methods for implementing Jacobi diagonalization algorithms have been recently proposed for both distributed memory and vector machines. These methods are naturally well suited to distributed memory and vector architectures because of their inherent parallelism and their abundance of vector operations. Also, one-sided methods require substantially less message passing than the two-sided methods, and thus can achieve higher efficiency. We describe in detail the use of the one-sided Jacobi rotation as opposed to the rotation used in the ``Hestenes'' algorithm; we perceive this difference to have been widely misunderstood. Furthermore the one-sided algorithm generalizes to other problems such as the nonsymmetric eigenvalue problem while the Hestenes algorithm does not. We discuss two new implementations for Jacobi sets for a ring connected array of processors and show their isomorphism to the round-robin ordering. Moreover, we show that two implementations produce Jacobi sets in identical orders up to a relabeling. These orderings are optimal in the sense that they complete each sweep in a minimum number of stages with minimal communication. We present implementation results of one-sided Jacobi algorithms using these orderings on the NCUBE/seven hypercube as well as the Intel iPSC/2 hypercube. Finally, we mention how other orderings, and can be, implemented. The number of nonisomorphic Jacobi sets has recently been shown to become infinite with increasing n. The work of this author was supported by National Science Foundation Grant CCR-8813493.

  12. 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

  13. Spectral algorithms for heterogeneous biological networks.

    PubMed

    McDonald, Martin; Higham, Desmond J; Vass, J Keith

    2012-11-01

    Spectral methods, which use information relating to eigenvectors, singular vectors and generalized singular vectors, help us to visualize and summarize sets of pairwise interactions. In this work, we motivate and discuss the use of spectral methods by taking a matrix computation view and applying concepts from applied linear algebra. We show that this unified approach is sufficiently flexible to allow multiple sources of network information to be combined. We illustrate the methods on microarray data arising from a large population-based study in human adipose tissue, combined with related information concerning metabolic pathways. PMID:23117863

  14. A novel ECDM-OFDM-PON architecture for next-generation optical access network.

    PubMed

    Zhang, Lijia; Xin, Xiangjun; Liu, Bo; Yu, Jianjun; Zhang, Qi

    2010-08-16

    This paper proposes a novel architecture for next-generation passive optical network (PON) based on electrical code division multiplexing orthogonal frequency division multiplexing (ECDM-OFDM) access. The feasibility of bidirectional transmission with the same wavelength has been experimentally demonstrated under this architecture. An error-free transmission of two PON channels has been successfully demonstrated in the experiment. PMID:20721227

  15. 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.

  16. 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

  17. 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

    2015-01-01

    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

  18. Fault location of underground distribution network based on RBF network optimized by improved PSO algorithm

    NASA Astrophysics Data System (ADS)

    Tian, Shu; Zhao, Min

    2013-03-01

    To solve the difficult problem that exists in the location of single-phase ground fault for coal mine underground distribution network, a fault location method using RBF network optimized by improved PSO algorithm based on the mapping relationship between wavelet packet transform modulus maxima of specific frequency bands transient state zero sequence current in the fault line and fault point position is presented. The simulation analysis results in the cases of different transition resistances and fault distances show that the RBF network optimized by improved PSO algorithm can obtain accurate and reliable fault location results, and the fault location perfor- mance is better than traditional RBF network.

  19. Adaptive NUC algorithm for uncooled IRFPA based on neural networks

    NASA Astrophysics Data System (ADS)

    Liu, Ziji; Jiang, Yadong; Lv, Jian; Zhu, Hongbin

    2010-10-01

    With developments in uncooled infrared plane array (UFPA) technology, many new advanced uncooled infrared sensors are used in defensive weapons, scientific research, industry and commercial applications. A major difference in imaging techniques between infrared IRFPA imaging system and a visible CCD camera is that, IRFPA need nonuniformity correction and dead pixel compensation, we usually called it infrared image pre-processing. Two-point or multi-point correction algorithms based on calibration commonly used may correct the non-uniformity of IRFPAs, but they are limited by pixel linearity and instability. Therefore, adaptive non-uniformity correction techniques are developed. Two of these adaptive non-uniformity correction algorithms are mostly discussed, one is based on temporal high-pass filter, and another is based on neural network. In this paper, a new NUC algorithm based on improved neural networks is introduced, and involves the compare result between improved neural networks and other adaptive correction techniques. A lot of different will discussed in different angle, like correction effects, calculation efficiency, hardware implementation and so on. According to the result and discussion, it could be concluding that the adaptive algorithm offers improved performance compared to traditional calibration mode techniques. This new algorithm not only provides better sensitivity, but also increases the system dynamic range. As the sensor application expended, it will be very useful in future infrared imaging systems.

  20. Recursive least-squares learning algorithms for neural networks

    SciTech Connect

    Lewis, P.S. ); Hwang, Jenq-Neng . Dept. of Electrical Engineering)

    1990-01-01

    This paper presents the development of a pair of recursive least squares (RLS) algorithms for online training of multilayer perceptrons, which are a class of feedforward artificial neural networks. These algorithms incorporate second order information about the training error surface in order to achieve faster learning rates than are possible using first order gradient descent algorithms such as the generalized delta rule. A least squares formulation is derived from a linearization of the training error function. Individual training pattern errors are linearized about the network parameters that were in effect when the pattern was presented. This permits the recursive solution of the least squares approximation, either via conventional RLS recursions or by recursive QR decomposition-based techniques. The computational complexity of the update is in the order of (N{sup 2}), where N is the number of network parameters. This is due to the estimation of the N {times} N inverse Hessian matrix. Less computationally intensive approximations of the RLS algorithms can be easily derived by using only block diagonal elements of this matrix, thereby partitioning the learning into independent sets. A simulation example is presented in which a neural network is trained to approximate a two dimensional Gaussian bump. In this example, RLS training required an order of magnitude fewer iterations on average (527) than did training with the generalized delta rule (6331). 14 refs., 3 figs.

  1. 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.

  2. A neural network algorithm for sea ice edge classification

    SciTech Connect

    Alhumaidi, S.M.; Ferguson, S.M.; Jones, W.L.; Park, J.D.

    1997-07-01

    The NASA Scatterometer (NSCAT), launched in August 1996, is designed to measure wind vectors over ice-free oceans. To prevent contamination f the wind measurements, by the presence of sea ice, algorithms based on neural network technology have been developed to classify ice-free ocean surfaces. Neural networks trained using polarized alone and polarized plus multi-azimuth look Ku-band backscatter are described. Algorithm skill in locating the sea ice edge around Antarctica is experimentally evaluated using backscatter data from the Seasat-A Satellite Scatterometer that operated in 1978. Comparisons between the algorithms demonstrate a slight advantage of combined polarization and multi-look over using co-polarized backscatter alone. Classification skill is evaluated by comparisons with surface truth (sea ice maps), subjective ice classification, and independent over lapping scatterometer measurements (consecutive revolutions).

  3. A robust regularization algorithm for polynomial networks for machine learning

    NASA Astrophysics Data System (ADS)

    Jaenisch, Holger M.; Handley, James W.

    2011-06-01

    We present an improvement to the fundamental Group Method of Data Handling (GMDH) Data Modeling algorithm that overcomes the parameter sensitivity to novel cases presented to derived networks. We achieve this result by regularization of the output and using a genetic weighting that selects intermediate models that do not exhibit divergence. The result is the derivation of multi-nested polynomial networks following the Kolmogorov-Gabor polynomial that are robust to mean estimators as well as novel exemplars for input. The full details of the algorithm are presented. We also introduce a new method for approximating GMDH in a single regression model using F, H, and G terms that automatically exports the answers as ordinary differential equations. The MathCAD 15 source code for all algorithms and results are provided.

  4. Service-Transport Separated Network Architecture Using MPLS-TP Technologies

    NASA Astrophysics Data System (ADS)

    Sakamoto, Kenichi; Ashi, Yoshihiro; Takase, Akihiko

    We propose a next-generation service-transport separated network architecture that uses packet technology. The transport network is responsible for the high speed and reliable bit transfer, and the service network is responsible for many types of network services. This separation makes it possible for the next-generation network to achieve both rapid bandwidth spreading and the speedy installation of new services. We also propose a next-generation transport network using the multi-protocol label switching transport profile (MPLS-TP) and optical networking technologies.

  5. A Compression Algorithm in Wireless Sensor Networks of Bearing Monitoring

    NASA Astrophysics Data System (ADS)

    Bin, Zheng; Qingfeng, Meng; Nan, Wang; Zhi, Li

    2011-07-01

    The energy consumption of wireless sensor networks (WSNs) is always an important problem in the application of wireless sensor networks. This paper proposes a data compression algorithm to reduce amount of data and energy consumption during the data transmission process in the on-line WSNs-based bearing monitoring system. The proposed compression algorithm is based on lifting wavelets, Zerotree coding and Hoffman coding. Among of that, 5/3 lifting wavelets is used for dividing data into different frequency bands to extract signal characteristics. Zerotree coding is applied to calculate the dynamic thresholds to retain the attribute data. The attribute data are then encoded by Hoffman coding to further enhance the compression ratio. In order to validate the algorithm, simulation is carried out by using Matlab. The result of simulation shows that the proposed algorithm is very suitable for the compression of bearing monitoring data. The algorithm has been successfully used in online WSNs-based bearing monitoring system, in which TI DSP TMS320F2812 is used to realize the algorithm.

  6. Estimation of distribution algorithm for resource allocation in green cooperative cognitive radio sensor networks.

    PubMed

    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

  7. 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

  8. A High-Speed Pipelined Degree-Computationless Modified Euclidean Algorithm Architecture for Reed-Solomon Decoders

    NASA Astrophysics Data System (ADS)

    Lee, Seungbeom; Lee, Hanho

    This paper presents a novel high-speed low-complexity pipelined degree-computationless modified Euclidean (pDCME) algorithm architecture for high-speed RS decoders. The pDCME algorithm allows elimination of the degree-computation so as to reduce hardware complexity and obtain high-speed processing. A high-speed RS decoder based on the pDCME algorithm has been designed and implemented with 0.13-μm CMOS standard cell technology in a supply voltage of 1.1V. The proposed RS decoder operates at a clock frequency of 660MHz and has a throughput of 5.3Gb/s. The proposed architecture requires approximately 15% fewer gate counts and a simpler control logic than architectures based on the popular modified Euclidean algorithm.

  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. 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. PMID:23140797

  12. 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. PMID:22319375

  13. Access protocols and network architectures for very high-speed optical fiber local area networks

    NASA Astrophysics Data System (ADS)

    Ganti, Sudhaker N. M.

    1993-10-01

    The single mode optical fiber possesses an enormous bandwidth of more than 30 THz in the low-loss optical region of 1.3 and 1.5 microns. Through wavelength division multiplexing (WDM), the optical fiber bandwidth can be divided into a set of high-speed channels, where each channel is assigned its own unique wavelength. An M x M passive optical star coupler is a simple broadcast medium, in which light energy incident at any input is uniformly coupled (or distributed) to all the outputs. Thus, a passive star along with the WDM channels can be used to configure a local area network (LAN). In this LAN, users require tunable devices to access a complete or a partial set of the WDM channels. Due to these multiple channels, many concurrent packet transmissions corresponding to different user pairs are possible and thus the total system throughput can be much higher than the data rates of each individual channel. To fairly arbitrate the data channels among the users, media access protocols are needed. Depending upon the number of data channels and the number of users, two possible situations arise. In the first case, the number of users is much larger than the number of data channels and in the second, the number of users equals the number of channels. In both cases, data channel contention may arise if multiple users access the same given channel and must be resolved. This thesis proposes media access protocols for passive optical star networks. All the proposed protocols are slotted in nature, i.e., the time axis on each channel is divided into slots. The well known Slotted-ALOHA and Reservation ALOHA protocols are extended to the multichannel network environment. The thesis also proposes switching protocols (equal number of channels and users), contention-based reservation protocols for this network architecture. To interconnect these star networks, a multi-control channel protocol is also proposed along with two interconnecting techniques. Since there are multiple data

  14. Genetic algorithm application in optimization of wireless sensor networks.

    PubMed

    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

  15. 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.

  16. 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

  17. An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks

    PubMed Central

    Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

    2016-01-01

    The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001

  18. An Efficient Supervised Training Algorithm for Multilayer Spiking Neural Networks.

    PubMed

    Xie, Xiurui; Qu, Hong; Liu, Guisong; Zhang, Malu; Kurths, Jürgen

    2016-01-01

    The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. PMID:27044001

  19. Metaheuristic Algorithms for Convolution Neural Network.

    PubMed

    Rere, L M Rasdi; 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

  20. 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

  1. Evaluation of the GPU architecture for the implementation of target detection algorithms for hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Trigueros-Espinosa, Blas; Vélez-Reyes, Miguel; Santiago-Santiago, Nayda G.; Rosario-Torres, Samuel

    2011-06-01

    Hyperspectral sensors can collect hundreds of images taken at different narrow and contiguously spaced spectral bands. This high-resolution spectral information can be used to identify materials and objects within the field of view of the sensor by their spectral signature, but this process may be computationally intensive due to the large data sizes generated by the hyperspectral sensors, typically hundreds of megabytes. This can be an important limitation for some applications where the detection process must be performed in real time (surveillance, explosive detection, etc.). In this work, we developed a parallel implementation of three state-ofthe- art target detection algorithms (RX algorithm, matched filter and adaptive matched subspace detector) using a graphics processing unit (GPU) based on the NVIDIA® CUDA™ architecture. In addition, a multi-core CPUbased implementation of each algorithm was developed to be used as a baseline for the speedups estimation. We evaluated the performance of the GPU-based implementations using an NVIDIA ® Tesla® C1060 GPU card, and the detection accuracy of the implemented algorithms was evaluated using a set of phantom images simulating traces of different materials on clothing. We achieved a maximum speedup in the GPU implementations of around 20x over a multicore CPU-based implementation, which suggests that applications for real-time detection of targets in HSI can greatly benefit from the performance of GPUs as processing hardware.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. Column generation algorithms for exact modularity maximization in networks.

    PubMed

    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)], 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)]. 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)] 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. PMID:21230350

  9. Scalable Architecture for Federated Translational Inquiries Network (SAFTINet) Technology Infrastructure for a Distributed Data Network

    PubMed Central

    Schilling, Lisa M.; Kwan, Bethany M.; Drolshagen, Charles T.; Hosokawa, Patrick W.; Brandt, Elias; Pace, Wilson D.; Uhrich, Christopher; Kamerick, Michael; Bunting, Aidan; Payne, Philip R.O.; Stephens, William E.; George, Joseph M.; Vance, Mark; Giacomini, Kelli; Braddy, Jason; Green, Mika K.; Kahn, Michael G.

    2013-01-01

    Introduction: Distributed Data Networks (DDNs) offer infrastructure solutions for sharing electronic health data from across disparate data sources to support comparative effectiveness research. Data sharing mechanisms must address technical and governance concerns stemming from network security and data disclosure laws and best practices, such as HIPAA. Methods: The Scalable Architecture for Federated Translational Inquiries Network (SAFTINet) deploys TRIAD grid technology, a common data model, detailed technical documentation, and custom software for data harmonization to facilitate data sharing in collaboration with stakeholders in the care of safety net populations. Data sharing partners host TRIAD grid nodes containing harmonized clinical data within their internal or hosted network environments. Authorized users can use a central web-based query system to request analytic data sets. Discussion: SAFTINet DDN infrastructure achieved a number of data sharing objectives, including scalable and sustainable systems for ensuring harmonized data structures and terminologies and secure distributed queries. Initial implementation challenges were resolved through iterative discussions, development and implementation of technical documentation, governance, and technology solutions. PMID:25848567

  10. Exploring multiple feature combination strategies with a recurrent neural network architecture for off-line handwriting recognition

    NASA Astrophysics Data System (ADS)

    Mioulet, L.; Bideault, G.; Chatelain, C.; Paquet, T.; Brunessaux, S.

    2015-01-01

    The BLSTM-CTC is a novel recurrent neural network architecture that has outperformed previous state of the art algorithms in tasks such as speech recognition or handwriting recognition. It has the ability to process long term dependencies in temporal signals in order to label unsegmented data. This paper describes different ways of combining features using a BLSTM-CTC architecture. Not only do we explore the low level combination (feature space combination) but we also explore high level combination (decoding combination) and mid-level (internal system representation combination). The results are compared on the RIMES word database. Our results show that the low level combination works best, thanks to the powerful data modeling of the LSTM neurons.

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

    PubMed

    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

  12. 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

  13. A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architectures.

    PubMed

    Kentzoglanakis, Kyriakos; Poole, Matthew

    2012-01-01

    In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques, namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network (RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is used for searching the discrete space of network architectures and PSO for searching the corresponding continuous space of RNN model parameters. We propose a novel solution construction process in the context of ACO for generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally, the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast space of network architectures. PMID:21576756

  14. 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.

  15. 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.

  16. GPS-Free Localization Algorithm for Wireless Sensor Networks

    PubMed Central

    Wang, Lei; Xu, Qingzheng

    2010-01-01

    Localization is one of the most fundamental problems in wireless sensor networks, since the locations of the sensor nodes are critical to both network operations and most application level tasks. A GPS-free localization scheme for wireless sensor networks is presented in this paper. First, we develop a standardized clustering-based approach for the local coordinate system formation wherein a multiplication factor is introduced to regulate the number of master and slave nodes and the degree of connectivity among master nodes. Second, using homogeneous coordinates, we derive a transformation matrix between two Cartesian coordinate systems to efficiently merge them into a global coordinate system and effectively overcome the flip ambiguity problem. The algorithm operates asynchronously without a centralized controller; and does not require that the location of the sensors be known a priori. A set of parameter-setting guidelines for the proposed algorithm is derived based on a probability model and the energy requirements are also investigated. A simulation analysis on a specific numerical example is conducted to validate the mathematical analytical results. We also compare the performance of the proposed algorithm under a variety multiplication factor, node density and node communication radius scenario. Experiments show that our algorithm outperforms existing mechanisms in terms of accuracy and convergence time. PMID:22219694

  17. GPS-free localization algorithm for wireless sensor networks.

    PubMed

    Wang, Lei; Xu, Qingzheng

    2010-01-01

    Localization is one of the most fundamental problems in wireless sensor networks, since the locations of the sensor nodes are critical to both network operations and most application level tasks. A GPS-free localization scheme for wireless sensor networks is presented in this paper. First, we develop a standardized clustering-based approach for the local coordinate system formation wherein a multiplication factor is introduced to regulate the number of master and slave nodes and the degree of connectivity among master nodes. Second, using homogeneous coordinates, we derive a transformation matrix between two Cartesian coordinate systems to efficiently merge them into a global coordinate system and effectively overcome the flip ambiguity problem. The algorithm operates asynchronously without a centralized controller; and does not require that the location of the sensors be known a priori. A set of parameter-setting guidelines for the proposed algorithm is derived based on a probability model and the energy requirements are also investigated. A simulation analysis on a specific numerical example is conducted to validate the mathematical analytical results. We also compare the performance of the proposed algorithm under a variety multiplication factor, node density and node communication radius scenario. Experiments show that our algorithm outperforms existing mechanisms in terms of accuracy and convergence time. PMID:22219694

  18. Predicting recurrent aphthous ulceration using genetic algorithms-optimized neural networks

    PubMed Central

    Dar-Odeh, Najla S; Alsmadi, Othman M; Bakri, Faris; Abu-Hammour, Zaer; Shehabi, Asem A; Al-Omiri, Mahmoud K; Abu-Hammad, Shatha M K; Al-Mashni, Hamzeh; Saeed, Mohammad B; Muqbil, Wael; Abu-Hammad, Osama A

    2010-01-01

    Objective To construct and optimize a neural network that is capable of predicting the occurrence of recurrent aphthous ulceration (RAU) based on a set of appropriate input data. Participants and methods Artificial neural networks (ANN) software employing genetic algorithms to optimize the architecture neural networks was used. Input and output data of 86 participants (predisposing factors and status of the participants with regards to recurrent aphthous ulceration) were used to construct and train the neural networks. The optimized neural networks were then tested using untrained data of a further 10 participants. Results The optimized neural network, which produced the most accurate predictions for the presence or absence of recurrent aphthous ulceration was found to employ: gender, hematological (with or without ferritin) and mycological data of the participants, frequency of tooth brushing, and consumption of vegetables and fruits. Conclusions Factors appearing to be related to recurrent aphthous ulceration and appropriate for use as input data to construct ANNs that predict recurrent aphthous ulceration were found to include the following: gender, hemoglobin, serum vitamin B12, serum ferritin, red cell folate, salivary candidal colony count, frequency of tooth brushing, and the number of fruits or vegetables consumed daily. PMID:21918622

  19. Bioinspired Evolutionary Algorithm Based for Improving Network Coverage in Wireless Sensor Networks

    PubMed Central

    Abbasi, Mohammadjavad; Bin Abd Latiff, Muhammad Shafie

    2014-01-01

    Wireless sensor networks (WSNs) include sensor nodes in which each node is able to monitor the physical area and send collected information to the base station for further analysis. The important key of WSNs is detection and coverage of target area which is provided by random deployment. This paper reviews and addresses various area detection and coverage problems in sensor network. This paper organizes many scenarios for applying sensor node movement for improving network coverage based on bioinspired evolutionary algorithm and explains the concern and objective of controlling sensor node coverage. We discuss area coverage and target detection model by evolutionary algorithm. PMID:24693247

  20. A new algorithm with coordinated node and link mapping for virtual network embedding based on LP relaxation

    NASA Astrophysics Data System (ADS)

    Gao, Xiujiao; Yu, Hongfang; Anand, Vishal; Sun, Gang; Di, Hao

    2010-12-01

    Network virtualization can help diversify the Internet by supporting multiple virtual network (VN) architectures on a shared substrate. Making efficient use of the underlying substrate network resources requires effective algorithms for virtual network embedding (VNE) that maps each virtual network (VN) to specific nodes and links in the substrate network. In this paper, we formulate the VNE problem as a mixed integer linear program (MILP), and then propose a progressively greedy VNE algorithm (PG-VNE) which has three main improvements over previous approaches. 1) Adding constraints after we relax the MILP to obtain STRICT_LP model which leads to better coordination between the node and link mapping stages, 2) Using the greedy idea to map virtual nodes and 3) Mapping virtual nodes progressively, i.e., mapping virtual nodes one by one. Simulation results show that PG-VNE algorithm realizes close coordination between node and link mapping stages and performs well in terms of revenue, cost and VN request acceptance ratio when compared with the well known D-ViNE and R-ViNE algorithms.

  1. 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.

  2. 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.

  3. 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.

  4. A fast neural-network algorithm for VLSI cell placement.

    PubMed

    Aykanat, Cevdet; Bultan, Tevfik; Haritaoğlu, Ismail

    1998-12-01

    Cell placement is an important phase of current VLSI circuit design styles such as standard cell, gate array, and Field Programmable Gate Array (FPGA). Although nondeterministic algorithms such as Simulated Annealing (SA) were successful in solving this problem, they are known to be slow. In this paper, a neural network algorithm is proposed that produces solutions as good as SA in substantially less time. This algorithm is based on Mean Field Annealing (MFA) technique, which was successfully applied to various combinatorial optimization problems. A MFA formulation for the cell placement problem is derived which can easily be applied to all VLSI design styles. To demonstrate that the proposed algorithm is applicable in practice, a detailed formulation for the FPGA design style is derived, and the layouts of several benchmark circuits are generated. The performance of the proposed cell placement algorithm is evaluated in comparison with commercial automated circuit design software Xilinx Automatic Place and Route (APR) which uses SA technique. Performance evaluation is conducted using ACM/SIGDA Design Automation benchmark circuits. Experimental results indicate that the proposed MFA algorithm produces comparable results with APR. However, MFA is almost 20 times faster than APR on the average. PMID:12662737

  5. 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.

  6. An architecture for covert unmanned/unattended network discovery

    NASA Astrophysics Data System (ADS)

    Giallorenzi, Thomas R.; Hyer, Kevin L.; Reeder, W. Bruce; Sylvester, Randal R.

    2005-10-01

    A common problem in modern military communication networks is node discovery. In order to form a robust and efficient network each node needs to notify the network of its existence and, where security policies allow, to report its location. Furthermore, the process of fast and covert new node identification and recognition can help prevent friendly fire incidents. Once a network is established, new nodes often need to join the existing network, and they need a way to do this without compromising their own security, or the security of the network that they are joining. In addition, an established network requires a method of discovering the existence of another disjoint network that has migrated into communication range, so that a cross-link can be established between the networks in order to form a larger network. This process of nodes "discovering" each other is called node discovery, and this provides a capability that has many applications. A good node discovery scheme for military communication related applications has a number of properties including: fast and reliable network entry, covertness, secure and jam proof signaling, and range extension compared to the primary communications link itself. For the purposes of this paper the mechanism that provides node discovery will be called the Discovery Waveform. The Discovery Waveform has many applications such as terminal discovery for wireless communications, node discovery for establishing networking, and seemingly unrelated applications such as bursting time critical data.

  7. 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: PMID:18434256

  8. Image enhancement algorithm based on improved lateral inhibition network

    NASA Astrophysics Data System (ADS)

    Yun, Haijiao; Wu, Zhiyong; Wang, Guanjun; Tong, Gang; Yang, Hua

    2016-05-01

    There is often substantial noise and blurred details in the images captured by cameras. To solve this problem, we propose a novel image enhancement algorithm combined with an improved lateral inhibition network. Firstly, we built a mathematical model of a lateral inhibition network in conjunction with biological visual perception; this model helped to realize enhanced contrast and improved edge definition in images. Secondly, we proposed that the adaptive lateral inhibition coefficient adhere to an exponential distribution thus making the model more flexible and more universal. Finally, we added median filtering and a compensation measure factor to build the framework with high pass filtering functionality thus eliminating image noise and improving edge contrast, addressing problems with blurred image edges. Our experimental results show that our algorithm is able to eliminate noise and the blurring phenomena, and enhance the details of visible and infrared images.

  9. An integrated architecture of adaptive neural network control for dynamic systems

    SciTech Connect

    Ke, Liu; Tokar, R.; Mcvey, B.

    1994-07-01

    In this study, an integrated neural network control architecture for nonlinear dynamic systems is presented. Most of the recent emphasis in the neural network control field has no error feedback as the control input which rises the adaptation problem. The integrated architecture in this paper combines feed forward control and error feedback adaptive control using neural networks. The paper reveals the different internal functionality of these two kinds of neural network controllers for certain input styles, e.g., state feedback and error feedback. Feed forward neural network controllers with state feedback establish fixed control mappings which can not adapt when model uncertainties present. With error feedbacks, neural network controllers learn the slopes or the gains respecting to the error feedbacks, which are error driven adaptive control systems. The results demonstrate that the two kinds of control scheme can be combined to realize their individual advantages. Testing with disturbances added to the plant shows good tracking and adaptation.

  10. Bicriteria Network Optimization Problem using Priority-based Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Gen, Mitsuo; Lin, Lin; Cheng, Runwei

    Network optimization is being an increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. In many applications, however, there are several criteria associated with traversing each edge of a network. For example, cost and flow measures are both important in the networks. As a result, there has been recent interest in solving Bicriteria Network Optimization Problem. The Bicriteria Network Optimization Problem is known a NP-hard. The efficient set of paths may be very large, possibly exponential in size. Thus the computational effort required to solve it can increase exponentially with the problem size in the worst case. In this paper, we propose a genetic algorithm (GA) approach used a priority-based chromosome for solving the bicriteria network optimization problem including maximum flow (MXF) model and minimum cost flow (MCF) model. The objective is to find the set of Pareto optimal solutions that give possible maximum flow with minimum cost. This paper also combines Adaptive Weight Approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. Computer simulations show the several numerical experiments by using some difficult-to-solve network design problems, and show the effectiveness of the proposed method.

  11. NML computation algorithms for tree-structured multinomial Bayesian networks.

    PubMed

    Kontkanen, Petri; Wettig, Hannes; Myllymäki, Petri

    2007-01-01

    Typical problems in bioinformatics involve large discrete datasets. Therefore, in order to apply statistical methods in such domains, it is important to develop efficient algorithms suitable for discrete data. The minimum description length (MDL) principle is a theoretically well-founded, general framework for performing statistical inference. The mathematical formalization of MDL is based on the normalized maximum likelihood (NML) distribution, which has several desirable theoretical properties. In the case of discrete data, straightforward computation of the NML distribution requires exponential time with respect to the sample size, since the definition involves a sum over all the possible data samples of a fixed size. In this paper, we first review some existing algorithms for efficient NML computation in the case of multinomial and naive Bayes model families. Then we proceed by extending these algorithms to more complex, tree-structured Bayesian networks. PMID:18382603

  12. 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.

  13. 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.

  14. Graph-balancing algorithms for average consensus over directed networks

    NASA Astrophysics Data System (ADS)

    Fan, Yuan; Han, Runzhe; Qiu, Jianbin

    2016-01-01

    Consensus strategies find extensive applications in coordination of robot groups and decision-making of agents. Since balanced graph plays an important role in the average consensus problem and many other coordination problems for directed communication networks, this work explores the conditions and algorithms for the digraph balancing problem. Based on the analysis of graph cycles, we prove that a digraph can be balanced if and only if the null space of its incidence matrix contains positive vectors. Then, based on this result and the corresponding analysis, two weight balance algorithms have been proposed, and the conditions for obtaining a unique balanced solution and a set of analytical results on weight balance problems have been introduced. Then, we point out the relationship between the weight balance problem and the features of the corresponding underlying Markov chain. Finally, two numerical examples are presented to verify the proposed algorithms.

  15. MAC protocol for ad hoc networks using a genetic algorithm.

    PubMed

    Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L; Reyna, Alberto

    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

  16. 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

  17. 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

  18. Private algorithms for the protected in social network search.

    PubMed

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

    2016-01-26

    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

  19. 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/ PMID:20154426

  20. Architectures and algorithms for all-optical 3D signal processing

    NASA Astrophysics Data System (ADS)

    Giglmayr, Josef

    1999-07-01

    All-optical signal processing by >= 2D lightwave circuits (LCs) is (i) aimed to allow the (later) inclusion of the frequency domain and is (ii) subject to photonic integration and thus the architectural and algorithmic framework has to be prepared carefully. Much work has been done in >= 2D algebraic system theory/modern control theory which has been applied in the electronic field of signal and image processing. For the application to modeling, analysis and design of the proposed 3D lightwave circuits (LCs) some elements are needed to describe and evalute the system efficiency as the number of system states of 3D LCs increases dramatically with regard to the number of i/o. Several problems, arising throughput such an attempt, are made transparent and solutions are proposed.

  1. A decentralized training algorithm for Echo State Networks in distributed big data applications.

    PubMed

    Scardapane, Simone; Wang, Dianhui; Panella, Massimo

    2016-06-01

    The current big data deluge requires innovative solutions for performing efficient inference on large, heterogeneous amounts of information. Apart from the known challenges deriving from high volume and velocity, real-world big data applications may impose additional technological constraints, including the need for a fully decentralized training architecture. While several alternatives exist for training feed-forward neural networks in such a distributed setting, less attention has been devoted to the case of decentralized training of recurrent neural networks (RNNs). In this paper, we propose such an algorithm for a class of RNNs known as Echo State Networks. The algorithm is based on the well-known Alternating Direction Method of Multipliers optimization procedure. It is formulated only in terms of local exchanges between neighboring agents, without reliance on a coordinating node. Additionally, it does not require the communication of training patterns, which is a crucial component in realistic big data implementations. Experimental results on large scale artificial datasets show that it compares favorably with a fully centralized implementation, in terms of speed, efficiency and generalization accuracy. PMID:26341005

  2. 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.

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

    SciTech Connect

    Carroll, C.C.; Youngblood, J.N.; Saha, A.

    1987-12-01

    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.

  4. Introduction to the special section on computer architectures and parallel algorithms for PAMI

    SciTech Connect

    Dyer, C.R.

    1989-03-01

    The topic of multiprocessor computer architectures and parallel algorithms for computer vision and related applications is not new, but researchers are now addressing both a wider scope of issues and emphasizing system integration. Recently, a wide variety of different systems have been designed, built, and tested on a range of image understanding tasks. An important goal beginning to be addressed is how to achieve high performance when a complete, integrated set of component vision processes are combined. The papers in this special section describe a number of approaches to improving the performance of vision architectures. Each paper uses a different model of parallel processing. The first four papers describe machines or chips which have been built, each exhibiting certain advantages for vision. One important distinction between these approaches is in terms of the number of processors used, defining the granularity of parallel processing. The first three papers also evaluate the performance of their systems on a suite of vision tasks covering several image representations and processing requirements.

  5. 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.

  6. Efficient Algorithm and Architecture of Critical-Band Transform for Low-Power Speech Applications

    NASA Astrophysics Data System (ADS)

    Wang, Chao; Gan, Woon-Seng

    2007-12-01

    An efficient algorithm and its corresponding VLSI architecture for the critical-band transform (CBT) are developed to approximate the critical-band filtering of the human ear. The CBT consists of a constant-bandwidth transform in the lower frequency range and a Brown constant-[InlineEquation not available: see fulltext.] transform (CQT) in the higher frequency range. The corresponding VLSI architecture is proposed to achieve significant power efficiency by reducing the computational complexity, using pipeline and parallel processing, and applying the supply voltage scaling technique. A 21-band Bark scale CBT processor with a sampling rate of 16 kHz is designed and simulated. Simulation results verify its suitability for performing short-time spectral analysis on speech. It has a better fitting on the human ear critical-band analysis, significantly fewer computations, and therefore is more energy-efficient than other methods. With a 0.35[InlineEquation not available: see fulltext.]m CMOS technology, it calculates a 160-point speech in 4.99 milliseconds at 234 kHz. The power dissipation is 15.6[InlineEquation not available: see fulltext.]W at 1.1 V. It achieves 82.1[InlineEquation not available: see fulltext.] power reduction as compared to a benchmark 256-point FFT processor.

  7. Control algorithms of SONET integrated self-healing networks

    NASA Astrophysics Data System (ADS)

    Hasegawa, Satoshi; Okaoue, Yasuyo; Egawa, Takashi; Sakauchi, Hideki

    1994-01-01

    As the deployment of high-speed fiber transmission systems has been accelerated, they are widely recognized as a firm infrastructure of information society. Under this circumstance, the importance of network survivability has been increasing rapidly in these days. In SONET, the self-healing networks have been highlighted as one of the most advanced mechanisms to realize SONET survivable networks. Several schemes have been proposed and studied actively due to a rapid progress on the development of highly intelligent NE's. Among them in this paper, a DCS based distributed self-healing network is discussed from a viewpoint of its control algorithms. Specifically, our self-healing algorithm called TRANS is explained in detail, which possesses such desirable features as providing fast and flexible restoration with line and path level restoration applied to an individual STS-1 channel, capability to handle multiple and even node failures, and so on. Both software simulation and hardware experiment verify that TRANS works properly in a real distributed environment, the result of which is shown in the paper. In addition, the combined use of TRANS and the ring restoration control is proposed taking into account the use in a practical SONET.

  8. Algorithmic aspects of topology control problems for ad hoc networks

    SciTech Connect

    Liu, R.; Lloyd, E. L.; Marathe, M. V.; Ramanathan, R.; Ravi, S. S.

    2002-01-01

    Topology control problems are concerned with the assignment of power values to nodes of an ad hoc network so that the power assignment leads to a graph topology satisfying some specified properties. This paper considers such problems under several optimization objectives, including minimizing the maximum power and minimizing the total power. A general approach leading to a polynomial algorithm is presented for minimizing maximum power for a class of graph properties, called monotone properties. The difficulty of generalizing the approach to properties that are not monoione is pointed out. Problems involving the minimization of total power are known to be NP-complete even for simple graph properties. A general approach that leads to an approximation algorithm for minimizing the total power for some monotone properties is presented. Using this approach, a new approximation algorithm for the problem of minimizing the total power for obtaining a 2-node-connected graph is obtained. It is shown that this algorithm provides a constant performance guarantee. Experimental results from an implementation of the approximation algorithm are also presented.

  9. Optimal neural network architecture selection: effects on computer-aided detection of mammographic microcalcifications

    NASA Astrophysics Data System (ADS)

    Gurcan, Metin N.; Chan, Heang-Ping; Sahiner, Berkman; Hadjiiski, Lubomir M.; Petrick, Nicholas; Helvie, Mark A.

    2002-05-01

    We evaluated the effectiveness of an optimal convolution neural network (CNN) architecture selected by simulated annealing for improving the performance of a computer-aided diagnosis (CAD) system designed for the detection of microcalcification clusters on digitized mammograms. The performances of the CAD programs with manually and optimally selected CNNs were compared using an independent test set. This set included 472 mammograms and contained 253 biopsy-proven malignant clusters. Free-response receiver operating characteristic (FROC) analysis was used for evaluation of the detection accuracy. At a false positive (FP) rate of 0.7 per image, the film-based sensitivity was 84.6% with the optimized CNN, in comparison with 77.2% with the manually selected CNN. If clusters having images in both craniocaudal and mediolateral oblique views were analyzed together and a cluster was considered to be detected when it was detected in one or both views, at 0.7 FPs/image, the sensitivity was 93.3% with the optimized CNN and 87.0% with the manually selected CNN. This study indicates that classification of true positive and FP signals is an important step of the CAD program and that the detection accuracy of the program can be considerably improved by optimizing this step with an automated optimization algorithm.

  10. Architectures and algorithms for digital image processing; Proceedings of the Meeting, Cannes, France, December 5, 6, 1985

    NASA Technical Reports Server (NTRS)

    Duff, Michael J. B. (Editor); Siegel, Howard J. (Editor); Corbett, Francis J. (Editor)

    1986-01-01

    The conference presents papers on the architectures, algorithms, and applications of image processing. Particular attention is given to a very large scale integration system for image reconstruction from projections, a prebuffer algorithm for instant display of volume data, and an adaptive image sequence filtering scheme based on motion detection. Papers are also presented on a simple, direct practical method of sensing local motion and analyzing local optical flow, image matching techniques, and an automated biological dosimetry system.

  11. 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. PMID:25535626

  12. Prolonging the Lifetime of Wireless Sensor Networks Interconnected to Fixed Network Using Hierarchical Energy Tree Based Routing Algorithm

    PubMed Central

    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. PMID:25535626

  13. 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.

  14. 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.

  15. The architecture of dynamic reservoir in the echo state network

    NASA Astrophysics Data System (ADS)

    Cui, Hongyan; Liu, Xiang; Li, Lixiang

    2012-09-01

    Echo state network (ESN) has recently attracted increasing interests because of its superior capability in modeling nonlinear dynamic systems. In the conventional echo state network model, its dynamic reservoir (DR) has a random and sparse topology, which is far from the real biological neural networks from both structural and functional perspectives. We hereby propose three novel types of echo state networks with new dynamic reservoir topologies based on complex network theory, i.e., with a small-world topology, a scale-free topology, and a mixture of small-world and scale-free topologies, respectively. We then analyze the relationship between the dynamic reservoir structure and its prediction capability. We utilize two commonly used time series to evaluate the prediction performance of the three proposed echo state networks and compare them to the conventional model. We also use independent and identically distributed time series to analyze the short-term memory and prediction precision of these echo state networks. Furthermore, we study the ratio of scale-free topology and the small-world topology in the mixed-topology network, and examine its influence on the performance of the echo state networks. Our simulation results show that the proposed echo state network models have better prediction capabilities, a wider spectral radius, but retain almost the same short-term memory capacity as compared to the conventional echo state network model. We also find that the smaller the ratio of the scale-free topology over the small-world topology, the better the memory capacities.

  16. The architecture of dynamic reservoir in the echo state network.

    PubMed

    Cui, Hongyan; Liu, Xiang; Li, Lixiang

    2012-09-01

    Echo state network (ESN) has recently attracted increasing interests because of its superior capability in modeling nonlinear dynamic systems. In the conventional echo state network model, its dynamic reservoir (DR) has a random and sparse topology, which is far from the real biological neural networks from both structural and functional perspectives. We hereby propose three novel types of echo state networks with new dynamic reservoir topologies based on complex network theory, i.e., with a small-world topology, a scale-free topology, and a mixture of small-world and scale-free topologies, respectively. We then analyze the relationship between the dynamic reservoir structure and its prediction capability. We utilize two commonly used time series to evaluate the prediction performance of the three proposed echo state networks and compare them to the conventional model. We also use independent and identically distributed time series to analyze the short-term memory and prediction precision of these echo state networks. Furthermore, we study the ratio of scale-free topology and the small-world topology in the mixed-topology network, and examine its influence on the performance of the echo state networks. Our simulation results show that the proposed echo state network models have better prediction capabilities, a wider spectral radius, but retain almost the same short-term memory capacity as compared to the conventional echo state network model. We also find that the smaller the ratio of the scale-free topology over the small-world topology, the better the memory capacities. PMID:23020466

  17. 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.

  18. 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.

  19. 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…

  20. A synergisitic Neural Network Soil Moisture Retrieval Algorithm for SMAP

    NASA Astrophysics Data System (ADS)

    Kolassa, J.; Reichle, R. H.; Gentine, P.; Prigent, C.; Aires, F.; Fang, B.

    2015-12-01

    A Neural Network (NN)-based algorithm is developed to retrieve surface soil moisture from Soil Moisture Active/Passive (SMAP) microwave observations. This statistical approach serves as an alternative to the official Radiative Transfer (RT) based SMAP retrieval algorithm, since it avoids an explicit formulation of the RT processes as well as the use of often uncertain or unavailable a priori knowledge for additional surface parameters. The NN algorithm is calibrated on observations from the SMAP radiometer and radar as well as surface soil moisture fields from the MERRA-2 reanalysis. To highlight different physical aspects of the satellite signals and to maximize the soil moisture information, different preprocessing techniques of the SMAP data are investigated. These include an analysis of radiometer polarization and diurnal indices to isolate the surface temperature contribution, as well as the radar co- and cross-polarized channels to account for vegetation effects. A major difference with respect to the official retrieval is the increased importance given to the information provided by the SMAP radar or other active sensors, utilizing not only the relative spatial structures, but also the absolute soil moisture information provided. The NN methodology combines multiple sensor observations in a data fusion approach and is thus able to fully exploit the complementarity of the information provided by the different instruments. The algorithm is used to compute global estimates of surface soil moisture and evaluated against retrieved soil moisture from SMOS as well as in situ observations from the International Soil Moisture Network (ISMN). The calibration on MERRA-2 data means that the NN retrieval algorithm functions as the model operator in a data assimilation framework yielding soil moisture estimates that are very compatible with the model. This could facilitate the assimilation of SMAP observations into land surface and numerical weather prediction models.

  1. Reduction of the effects of the communication delays in scientific algorithms on message passing MIMD architectures

    NASA Technical Reports Server (NTRS)

    Saltz, J. H.; Naik, V. K.; Nicol, D. M.

    1986-01-01

    The efficient implementation of algorithms on multiprocessor machines requires that the effects of communication delays be minimized. The effects of these delays on the performance of a model problem on a hypercube multiprocessor architecture is investigated and methods are developed for increasing algorithm efficiency. The model problem under investigation is the solution by red-black Successive Over Relaxation YOUN71 of the heat equation; most of the techniques described here also apply equally well to the solution of elliptic partial differential equations by red-black or multicolor SOR methods. Methods for reducing communication traffic and overhead on a multiprocessor are identified and results of testing these methods on the Intel iPSC Hypercube reported. Methods for partitioning a problem's domain across processors, for reducing communication traffic during a global convergence check, for reducing the number of global convergence checks employed during an iteration, and for concurrently iterating on multiple time-steps in a time-dependent problem. Empirical results show that use of these models can markedly reduce a numewrical problem's execution time.

  2. Network-Attached Solid-State Recorder Architecture

    NASA Technical Reports Server (NTRS)

    Cox, Brian

    2008-01-01

    A document discusses placing memory modules on the high-speed serial interconnect, which is used by a spacecraft s computer elements for inter-processor communications, to allow all multiple computer system architectures to access the spacecraft data storage at the same time. Each memory board is identical electrically and receives its bus ID upon connection to the system. The computer elements are configured in a similar fashion. The architecture allows for multiple memory boards to be accessed simultaneously by different computer elements, and results in a scalable, strong, fault-tolerant system. The IEEE-1393 ring bus can be routed so that multiple card failures can occur and the mass memory storage will still function.

  3. 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.

  4. A shortest path algorithm for satellite time-varying topological network

    NASA Astrophysics Data System (ADS)

    Zhang, Tao; Liu, Zhongkan; Zhuang, Jun

    2005-11-01

    Mobile satellite network is a special time-varying network. It is different from the classical fixed network and other time-dependent networks which have been studied. Therefore some classical network theories, such as the shortest path algorithm, can not be applied to it availably. However, no study about its shortest path problem has been done. In this paper, based on the proposed model of satellite time-varying topological network, the classical shortest path algorithm of fixed network, such as the Dijkstra algorithm, is proved to be restrictive when it is applied in satellite network. Here, a novel shortest path algorithm for satellite time-varying topological network is given and optimized. Correlative simulation indicates that this algorithm can be effectively applied to the satellite time-varying topological network.

  5. 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.

  6. 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.

  7. 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.

  8. Call admission algorithms in multiservice and multiclass ATM network

    NASA Astrophysics Data System (ADS)

    Hamma, Salima; Hebuterne, Gerard

    2004-09-01

    The introduction of new ATM service categories increases the benefits of ATM, making the technology suitable for a virtually unlimited range of applications. Connection Admission Control (CAC) is defined as the set of actions taken by the network during the call (virtual connection) set-up phase, or during call re-negotiation phase, to determine whether a connection request can be accepted or rejected. Network resources (port bandwidth and buffer space) are reserved to the incoming connection at each switching element traversed, if so required, by the service category. The major focus of this paper is call admission in the context of multi-service, multi-class ATM networks. Several strategies suggesting rules on bandwidth sharing are found in the litterature. This study investigates particularly the Complete Sharing approach. Two service categories are concerned, namely, Constant Bit rate/Deterministic Bit Rate (CBR/DBR) and Variable Bit Rate/Statistical Bit Rate (VBR/SBR). Each service category is represented by a set of call classes corresponding to different bandwidth needs. We propose two algorithms to solve the underlying Markovian system: Product-form and Recursive solutions. A performance study based on the latter algorithm is implemented. We analyze the results of this very sharing strategy and set the not-to-violate limits for a beneficial use of it.

  9. 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.

  10. Quantum cryptography on multi-user network architectures

    NASA Astrophysics Data System (ADS)

    Kumavor, Patrick D.; Beal, Alan C.; Yelin, Susanne; Donkor, Eric; Wang, Bing C.

    2006-05-01

    Quantum cryptography applies the uncertainty principle and the no-cloning theorem to allow to parties to share a secret key over an ultra-secure link. Present quantum cryptography technologies provide encryption key distribution only between two users. However, practical implementations of encryption key distribution schemes require establishing secure quantum communications amongst multiple users. This paper looks at some of the advantages and drawbacks of some common network topologies that could be used in sending cryptographic keys across a network consisting of multiple users. These topologies are the star, ring, and bus networks. Their performances are compared and analyzed using quantum bit error rate analysis. The paper also presents an experimental demonstration of a six-user quantum key distribution network implemented on a bus topology.

  11. Optimized approximation algorithm in neural networks without overfitting.

    PubMed

    Liu, Yinyin; Starzyk, Janusz A; Zhu, Zhen

    2008-06-01

    In this paper, an optimized approximation algorithm (OAA) is proposed to address the overfitting problem in function approximation using neural networks (NNs). The optimized approximation algorithm avoids overfitting by means of a novel and effective stopping criterion based on the estimation of the signal-to-noise-ratio figure (SNRF). Using SNRF, which checks the goodness-of-fit in the approximation, overfitting can be automatically detected from the training error only without use of a separate validation set. The algorithm has been applied to problems of optimizing the number of hidden neurons in a multilayer perceptron (MLP) and optimizing the number of learning epochs in MLP's backpropagation training using both synthetic and benchmark data sets. The OAA algorithm can also be utilized in the optimization of other parameters of NNs. In addition, it can be applied to the problem of function approximation using any kind of basis functions, or to the problem of learning model selection when overfitting needs to be considered. PMID:18541499

  12. Novel Spectrum Sensing Algorithms for OFDM Cognitive Radio Networks.

    PubMed

    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

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. LBR: Load Balancing Routing Algorithm for Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Daabaj, Khaled; Dixon, Mike; Koziniec, Terry

    2010-06-01

    Homogeneous wireless sensor networks (WSNs) are organized using identical sensor nodes, but the nature of WSNs operations results in an imbalanced workload on gateway sensor nodes which may lead to a hot-spot or routing hole problem. The routing hole problem can be considered as a natural result of the tree-based routing schemes that are widely used in WSNs, where all nodes construct a multi-hop routing tree to a centralized root, e.g., a gateway or base station. For example, sensor nodes on the routing path and closer to the base station deplete their own energy faster than other nodes, or sensor nodes with the best link state to the base station are overloaded with traffic from the rest of the network and experience a faster energy depletion rate than their peers. Routing protocols for WSNs are reliability-oriented and their use of reliability metric to avoid unreliable links makes the routing scheme worse. However, none of these reliability oriented routing protocols explicitly uses load balancing in their routing schemes. Since improving network lifetime is a fundamental challenge of WSNs, we present, in this chapter, a novel, energy-wise, load balancing routing (LBR) algorithm that addresses load balancing in an energy efficient manner by maintaining a reliable set of parent nodes. This allows sensor nodes to quickly find a new parent upon parent loss due to the existing of node failure or energy hole. The proposed routing algorithm is tested using simulations and the results demonstrate that it outperforms the MultiHopLQI reliability based routing algorithm.

  18. Efficient multiprocessor architecture for digital signal processing

    SciTech Connect

    Auguin, M.; Boeri, F.

    1982-01-01

    There is a continuing pressure of better processing performances in numerical signal processing. Effective utilization of LSI semiconductor technology allows the consideration of multiprocessor architectures. The problem of interconnecting the components of the architecture arises. The authors describe a control algorithm of the Benes interconnection network in a asynchronous multiprocessor system. A simulation study of the time-shared bus, of the omega network, of the benes network and of the crossbar network gives a comparison of performances. 8 references.

  19. A dynamic clustering algorithm in wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Wang, Rui; Liang, Yan; Pan, Quan; Wang, Quan; Cheng, Yongmei

    2005-11-01

    It is essential to prolong the lifetime of wireless sensor networks (WSN) via effective cooperation of its sensor nodes. Here, a dynamic clustering algorithm, named DCA, is presented to optimally and dynamically select the micro-sensor nodes to construct a dynamic sensor cluster at each time based on the integrated performance index including information acquirement and energy consumption. In distributed target tracking with WSN, the DCA can avoid the problem of "too frequent cluster head (CH) switches", save more than 80% energy and remain almost same tracking accuracy, compared with the information-driven sensor querying (IDSQ).

  20. Applications of genetic algorithms and neural networks to interatomic potentials

    NASA Astrophysics Data System (ADS)

    Hobday, Steven; Smith, Roger; BelBruno, Joe

    1999-06-01

    Applications of two modern artificial intelligence (AI) techniques, genetic algorithms (GA) and neural networks (NN) to computer simulations are reported. It is shown that the GA are very useful tools for determining the minimum energy structures of clusters of atoms described by interatomic potential functions and generally outperform other optimisation methods for this task. A number of applications are given including covalent, and close packed structures of single or multi-component atomic species. It is also shown that (many body) interatomic potential functions for multi-component systems can be derived by training a specially constructed NN on a variety of structural data.

  1. 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

  2. Genes and networks regulating root anatomy and architecture.

    PubMed

    Wachsman, Guy; Sparks, Erin E; Benfey, Philip N

    2015-10-01

    The root is an excellent model for studying developmental processes that underlie plant anatomy and architecture. Its modular structure, the lack of cell movement and relative accessibility to microscopic visualization facilitate research in a number of areas of plant biology. In this review, we describe several examples that demonstrate how cell type-specific developmental mechanisms determine cell fate and the formation of defined tissues with unique characteristics. In the last 10 yr, advances in genome-wide technologies have led to the sequencing of thousands of plant genomes, transcriptomes and proteomes. In parallel with the development of these high-throughput technologies, biologists have had to establish computational, statistical and bioinformatic tools that can deal with the wealth of data generated by them. These resources provide a foundation for posing more complex questions about molecular interactions, and have led to the discovery of new mechanisms that control phenotypic differences. Here we review several recent studies that shed new light on developmental processes, which are involved in establishing root anatomy and architecture. We highlight the power of combining large-scale experiments with classical techniques to uncover new pathways in root development. PMID:25989832

  3. 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.

  4. Personal Navigation Algorithms Based on Wireless Networks and Inertial Sensors

    NASA Astrophysics Data System (ADS)

    Kaňa, Zdenek; Bradáč, Zdenek; Fiedler, Petr

    2014-08-01

    The work aims at a development of positioning algorithm suitable for low-cost indoor or urban pedestrian navigation application. The sensor fusion was applied to increase the localization accuracy. Due to required low application cost only low grade inertial sensors and wireless network based ranging were taken into account. The wireless network was assumed to be preinstalled due to other required functionality (for example: building control) therefore only received signal strength (RSS) range measurement technique was considered. Wireless channel loss mapping method was proposed to overcome the natural uncertainties and restrictions in the RSS range measurements The available sensor and environment models are summarized first and the most appropriate ones are selected secondly. Their effective and novel application in the navigation task, and favorable fusion (Particle filtering) of all available information are the main objectives of this thesis.

  5. 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.

  6. 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

  7. 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.

  8. 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. PMID:23193796

  9. 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.

  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. Comparison of Lauritzen-Spiegelhalter and successive restrictions algorithms for computing probability distributions in Bayesian networks

    NASA Astrophysics Data System (ADS)

    Smail, Linda

    2016-06-01

    The basic task of any probabilistic inference system in Bayesian networks is computing the posterior probability distribution for a subset or subsets of random variables, given values or evidence for some other variables from the same Bayesian network. Many methods and algorithms have been developed to exact and approximate inference in Bayesian networks. This work compares two exact inference methods in Bayesian networks-Lauritzen-Spiegelhalter and the successive restrictions algorithm-from the perspective of computational efficiency. The two methods were applied for comparison to a Chest Clinic Bayesian Network. Results indicate that the successive restrictions algorithm shows more computational efficiency than the Lauritzen-Spiegelhalter algorithm.

  12. Trusted Network Selection using SAW and TOPSIS Algorithms for Heterogeneous Wireless Networks

    NASA Astrophysics Data System (ADS)

    Savitha, K.; Chandrasekar, C.

    2011-07-01

    Seamless continuity is the main goal in fourth generation Wireless networks (FGWNs), to achieve this "HANDOVER" technique is used, when a mobile terminal(MT) is in overlapping area for service continuity, Handover mechanism are mainly used. In Heterogeneous wireless networks main challenge is continual connection among the different networks like WiFi, WiMax, WLAN, WPAN etc. In this paper, Vertical handover decision schemes are compared and Multi Attribute Decision Making (MADM) is used to choose the best network from the available Visitor networks (VTs) for the continuous connection by the mobile terminal. In our work we mainly concentrated to the handover decision phase and to reduce the processing delay in the period of handover. MADM algorithms SAW and TOPSIS where compared to reduce the processing delay by using NS2 to evaluate the parameters for processing delay.

  13. An Architecture for Performance Optimization in a Collaborative Knowledge-Based Approach for Wireless Sensor Networks

    PubMed Central

    Gadeo-Martos, Manuel Angel; Fernandez-Prieto, Jose Angel; Canada-Bago, Joaquin; Velasco, Juan Ramon

    2011-01-01

    Over the past few years, Intelligent Spaces (ISs) have received the attention of many Wireless Sensor Network researchers. Recently, several studies have been devoted to identify their common capacities and to set up ISs over these networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks for the purpose of implementing ISs. This work presents a distributed architecture proposal for collaborative Fuzzy Rule-Based Systems embedded in Wireless Sensor Networks, which has been designed to optimize the implementation of ISs. This architecture includes the following: (a) an optimized design for the inference engine; (b) a visual interface; (c) a module to reduce the redundancy and complexity of the knowledge bases; (d) a module to evaluate the accuracy of the new knowledge base; (e) a module to adapt the format of the rules to the structure used by the inference engine; and (f) a communications protocol. As a real-world application of this architecture and the proposed methodologies, we show an application to the problem of modeling two plagues of the olive tree: prays (olive moth, Prays oleae Bern.) and repilo (caused by the fungus Spilocaea oleagina). The results show that the architecture presented in this paper significantly decreases the consumption of resources (memory, CPU and battery) without a substantial decrease in the accuracy of the inferred values. PMID:22163687

  14. Genetic architecture and regulatory networks in oilseed development

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Genetic analysis of global gene expression level variation provides evidence for transcriptional regulators and gene network relationships. Plant seeds are an important source of oil and protein, and a genome-wide assessment of transcriptional regulation during seed development offers insight into t...

  15. Signatures of arithmetic simplicity in metabolic network architecture.

    PubMed

    Riehl, William J; Krapivsky, Paul L; Redner, Sidney; Segrè, Daniel

    2010-04-01

    Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that properties similar to those predicted for the artificial chemistry hold also for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity. PMID:20369010

  16. Signatures of Arithmetic Simplicity in Metabolic Network Architecture

    PubMed Central

    Riehl, William J.; Krapivsky, Paul L.; Redner, Sidney; Segrè, Daniel

    2010-01-01

    Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that properties similar to those predicted for the artificial chemistry hold also for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity. PMID:20369010

  17. 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 C&T's development of…

  18. 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

  19. 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

  20. 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.

  1. Neural Networks Art: Solving Problems with Multiple Solutions and New Teaching Algorithm

    PubMed Central

    Dmitrienko, V. D; Zakovorotnyi, A. Yu.; Leonov, S. Yu.; Khavina, I. P

    2014-01-01

    A new discrete neural networks adaptive resonance theory (ART), which allows solving problems with multiple solutions, is developed. New algorithms neural networks teaching ART to prevent degradation and reproduction classes at training noisy input data is developed. Proposed learning algorithms discrete ART networks, allowing obtaining different classification methods of input. PMID:25246988

  2. 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. PMID:24592200

  3. Neural-network-biased genetic algorithms for materials design

    NASA Astrophysics Data System (ADS)

    Patra, Tarak; Meenakshisundaram, Venkatesh; Simmons, David

    Machine learning tools have been progressively adopted by the materials science community to accelerate design of materials with targeted properties. However, in the search for new materials exhibiting properties and performance beyond that previously achieved, machine learning approaches are frequently limited by two major shortcomings. First, they are intrinsically interpolative. They are therefore 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 the availability of large datasets, which in some fields are not available and would be prohibitively expensive to produce. Here we describe a new strategy for combining genetic algorithms, neural networks and other machine learning tools, and molecular simulation to discover materials with extremal properties in the absence of pre-existing data. Predictions from progressively constructed machine learning tools are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct molecular dynamics simulation. We survey several initial materials design problems we have addressed with this framework and compare its performance to that of standard genetic algorithm approaches. We acknowledge the W. M. Keck Foundation for support of this work.

  4. Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

    PubMed Central

    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. PMID:24592200

  5. Combining neural networks and genetic algorithms for hydrological flow forecasting

    NASA Astrophysics Data System (ADS)

    Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr

    2010-05-01

    We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and

  6. 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

  7. 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.

  8. 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).

  9. Heritability of the network architecture of intrinsic brain functional connectivity.

    PubMed

    Sinclair, Benjamin; Hansell, Narelle K; Blokland, Gabriëlla A M; Martin, Nicholas G; Thompson, Paul M; Breakspear, Michael; de Zubicaray, Greig I; Wright, Margaret J; McMahon, Katie L

    2015-11-01

    The brain's functional network exhibits many features facilitating functional specialization, integration, and robustness to attack. Using graph theory to characterize brain networks, studies demonstrate their small-world, modular, and "rich-club" properties, with deviations reported in many common neuropathological conditions. Here we estimate the heritability of five widely used graph theoretical metrics (mean clustering coefficient (γ), modularity (Q), rich-club coefficient (ϕnorm), global efficiency (λ), small-worldness (σ)) over a range of connection densities (k=5-25%) in a large cohort of twins (N=592, 84 MZ and 89 DZ twin pairs, 246 single twins, age 23 ± 2.5). We also considered the effects of global signal regression (GSR). We found that the graph metrics were moderately influenced by genetic factors h(2) (γ=47-59%, Q=38-59%, ϕnorm=0-29%, λ=52-64%, σ=51-59%) at lower connection densities (≤ 15%), and when global signal regression was implemented, heritability estimates decreased substantially h(2) (γ=0-26%, Q=0-28%, ϕnorm=0%, λ=23-30%, σ=0-27%). Distinct network features were phenotypically correlated (|r|=0.15-0.81), and γ, Q, and λ were found to be influenced by overlapping genetic factors. Our findings suggest that these metrics may be potential endophenotypes for psychiatric disease and suitable for genetic association studies, but that genetic effects must be interpreted with respect to methodological choices. PMID:26226088

  10. A general inside-out routing algorithm for a class of rearrangeable networks

    SciTech Connect

    Seo, S.W.; Feng, T.

    1994-12-31

    In this paper, we present a generalized version of the routing algorithm for a class of 2log{sub 2}N-stage networks which are made by concatenating two log{sub 2}N-stage blocking networks. We show that the generalized algorithm can also cover a class of (2log{sub 2}N-1)-stage networks. It is shown that the inside-out algorithm is a more general algorithm which covers a large class of inherently symmetric rearrangeable networks, including the Benes and its equivalent networks. Moreover, it is shown that the time complexity of the algorithm is in O(N), which is superior to that of the looping algorithm. The algorithm is discussed using a graph representation of the network and its connectivity properties are shown by a graph describing rule. To show that the algorithm covers a class of 2log{sub 2}N-stage networks, we introduce the concept of a base-network. These base-networks satisfy some common connectivity properties, and we show that any concatenation of two base-networks can be routed by our new algorithm.

  11. Node Non-Uniform Deployment Based on Clustering Algorithm for Underwater Sensor Networks.

    PubMed

    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

  12. 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

  13. 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.

  14. 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. PMID:27575119

  15. Architectural impact of FDDI network on scheduling hard real-time traffic

    NASA Technical Reports Server (NTRS)

    Agrawal, Gopal; Chen, Baio; Zhao, Wei; Davari, Sadegh

    1991-01-01

    The architectural impact on guaranteeing synchronous message deadlines in FDDI (Fiber Distributed Data Interface) token ring networks is examined. The FDDI network does not have facility to support (global) priority arbitration which is a useful facility for scheduling hard real time activities. As a result, it was found that the worst case utilization of synchronous traffic in an FDDI network can be far less than that in a centralized single processor system. Nevertheless, it is proposed and analyzed that a scheduling method can guarantee deadlines of synchronous messages having traffic utilization up to 33 pct., the highest to date.

  16. 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.

  17. Architecture-dependent signal conduction in model networks of endothelial cells

    NASA Astrophysics Data System (ADS)

    Deymier, Pierre A.; Eray, Mete; Deymier, Martin J.; Runge, Keith; Hoying, James B.; Vasseur, Jérome O.

    2010-04-01

    Signal conduction between endothelial cells along the walls of vessels appears to play an important role in circulatory function. A recently developed approach to calculate analytically the spectrum of propagating compositional waves in models of multicellular architectures is extended to study putative signal conduction dynamics across networks of endothelial cells. Here, compositional waves originate from negative feedback loops, such as between Ca2+ and inositol triphosphate (IP3) in endothelial cells, and are shaped by their connection topologies. We consider models of networks constituted of a main chain of endothelial cells and multiple side chains. The resulting transmission spectra encode information concerning the position and size of the side branches in the form of gaps. This observation suggests that endothelial cell networks may be able to “communicate” information regarding long-range order in their architecture.

  18. An Object-Oriented Network-Centric Software Architecture for Physical Computing

    NASA Astrophysics Data System (ADS)

    Palmer, Richard

    1997-08-01

    Recent developments in object-oriented computer languages and infrastructure such as the Internet, Web browsers, and the like provide an opportunity to define a more productive computational environment for scientific programming that is based more closely on the underlying mathematics describing physics than traditional programming languages such as FORTRAN or C++. In this talk I describe an object-oriented software architecture for representing physical problems that includes classes for such common mathematical objects as geometry, boundary conditions, partial differential and integral equations, discretization and numerical solution methods, etc. In practice, a scientific program written using this architecture looks remarkably like the mathematics used to understand the problem, is typically an order of magnitude smaller than traditional FORTRAN or C++ codes, and hence easier to understand, debug, describe, etc. All objects in this architecture are ``network-enabled,'' which means that components of a software solution to a physical problem can be transparently loaded from anywhere on the Internet or other global network. The architecture is expressed as an ``API,'' or application programmers interface specification, with reference embeddings in Java, Python, and C++. A C++ class library for an early version of this API has been implemented for machines ranging from PC's to the IBM SP2, meaning that phidentical codes run on all architectures.

  19. Genetic networks specifying the functional architecture of orientation domains in V1

    NASA Astrophysics Data System (ADS)

    Liedtke, Joscha; Wolf, Fred

    2015-03-01

    Although genetic information is critically important for brain development and structure, it is widely believed that neocortical functional architecture is largely shaped by activity dependent mechanisms. Here we show theoretically that mathematical models of genetic networks of principal neurons interacting by long range axonal morphogen transport can generate morphogen patterns that exactly prescribe the functional architecture of the primary visual cortex (V1) as experimentally observed. We analyze in detail an example genetic network that encodes the functional architecture of V1 by a dynamically generated morphogen pattern. We use analytical methods from weakly non-linear analysis [Cross & Hohenberg 1993] complemented by numerical simulations to obtain solutions of the model. In particular we find that the pinwheel statistics are in quantitative agreement with high precision experimental measurements [Kaschube et al. 2010]. This theory opens a novel perspective on the experimentally observed robustness of V1's architecture against radically abnormal developmental conditions such a dark rearing [White et al. 2001]. Furthermore, it provides for the first time a scheme how the pattern of a complex cortical architecture can be specified using only a small genetic bandwidth.

  20. 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. PMID:24039752