Inference and optimization of real edges on sparse graphs:A statistical physics perspective

Inference and optimization of real-value edge variables in sparse graphs are studied using the Bethe approximation and replica method of statistical physics. Equilibrium states of general energy functions involving a large set of real edge variables that interact at the network nodes are obtained in various cases. When applied to the representative problem of network resource allocation, efficient distributed algorithms are also devised. Scaling properties with respect to the network connectivity and the resource availability are found, and links to probabilistic Bayesian approximation methods are established. Different cost measures are considered and algorithmic solutions in the various cases are devised and examined numerically. Simulation results are in full agreement with the theory. © 2007 The American Physical Society.

Optical packet transmission in 42.6 Gbit/s wavelength-division-multiplexed clockwork-routed networks

The use of amplitude-modulated phase-shift-keyed (AM-PSK) optical data transmission is investigated in a sequence of concatenated links in a wavelength-division-multiplexed clockwork-routed network. The narrower channel spacing made possible by using AM-PSK format allows the network to contain a greater number of network nodes. Full differential precoding at the packet source reduces the amount of high-speed electronics required in the network and also offers simplified header recognition and time-to-live mechanisms.

Competition for shortest paths on sparse graphs

Optimal paths connecting randomly selected network nodes and fixed routers are studied analytically in the presence of a nonlinear overlap cost that penalizes congestion. Routing becomes more difficult as the number of selected nodes increases and exhibits ergodicity breaking in the case of multiple routers. The ground state of such systems reveals nonmonotonic complex behaviors in average path length and algorithmic convergence, depending on the network topology, and densities of communicating nodes and routers. A distributed linearly scalable routing algorithm is also devised. © 2012 American Physical Society.

Performance optimisation of the MAC protocol with multiple contention slots in MIMO ad hoc networks

The multiple-input multiple-output (MIMO) technique can be used to improve the performance of ad hoc networks. Various medium access control (MAC) protocols with multiple contention slots have been proposed to exploit spatial multiplexing for increasing the transport throughput of MIMO ad hoc networks. However, the existence of multiple request-to-send/clear-to-send (RTS/CTS) contention slots represents a severe overhead that limits the improvement on transport throughput achieved by spatial multiplexing. In addition, when the number of contention slots is fixed, the efficiency of RTS/CTS contention is affected by the transmitting power of network nodes. In this study, a joint optimisation scheme on both transmitting power and contention slots number for maximising the transport throughput is presented. This includes the establishment of an analytical model of a simplified MAC protocol with multiple contention slots, the derivation of transport throughput as a function of both transmitting power and the number of contention slots, and the optimisation process based on the transport throughput formula derived. The analytical results obtained, verified by simulation, show that much higher transport throughput can be achieved using the joint optimisation scheme proposed, compared with the non-optimised cases and the results previously reported.

Resource allocation in multi-user telecommunication networks

This dissertation discussed resource allocation mechanisms in several network topologies including infrastructure wireless network, non-infrastructure wireless network and wire-cum-wireless network. Different networks may have different resource constrains. Based on actual technologies and implementation models, utility function, game theory and a modern control algorithm have been introduced to balance power, bandwidth and customers' satisfaction in the system. ^ In infrastructure wireless networks, utility function was used in the Third Generation (3G) cellular network and the network was trying to maximize the total utility. In this dissertation, revenue maximization was set as an objective. Compared with the previous work on utility maximization, it is more practical to implement revenue maximization by the cellular network operators. The pricing strategies were studied and the algorithms were given to find the optimal price combination of power and rate to maximize the profit without degrading the Quality of Service (QoS) performance. ^ In non-infrastructure wireless networks, power capacity is limited by the small size of the nodes. In such a network, nodes need to transmit traffic not only for themselves but also for their neighbors, so power management become the most important issue for the network overall performance. Our innovative routing algorithm based on utility function, sets up a flexible framework for different users with different concerns in the same network. This algorithm allows users to make trade offs between multiple resource parameters. Its flexibility makes it a suitable solution for the large scale non-infrastructure network. This dissertation also covers non-cooperation problems. Through combining game theory and utility function, equilibrium points could be found among rational users which can enhance the cooperation in the network. ^ Finally, a wire-cum-wireless network architecture was introduced. This network architecture can support multiple services over multiple networks with smart resource allocation methods. Although a SONET-to-WiMAX case was used for the analysis, the mathematic procedure and resource allocation scheme could be universal solutions for all infrastructure, non-infrastructure and combined networks. ^

Reconstrução e reconhecimento de imagens binárias utilizando o algoritmo Máquina de Boltzmann

The objective of this work is to use algorithms known as Boltzmann Machine to rebuild and classify patterns as images. This algorithm has a similar structure to that of an Artificial Neural Network but network nodes have stochastic and probabilistic decisions. This work presents the theoretical framework of the main Artificial Neural Networks, General Boltzmann Machine algorithm and a variation of this algorithm known as Restricted Boltzmann Machine. Computer simulations are performed comparing algorithms Artificial Neural Network Backpropagation with these algorithms Boltzmann General Machine and Machine Restricted Boltzmann. Through computer simulations are analyzed executions times of the different described algorithms and bit hit percentage of trained patterns that are later reconstructed. Finally, they used binary images with and without noise in training Restricted Boltzmann Machine algorithm, these images are reconstructed and classified according to the bit hit percentage in the reconstruction of the images. The Boltzmann machine algorithms were able to classify patterns trained and showed excellent results in the reconstruction of the standards code faster runtime and thus can be used in applications such as image recognition.

Model predictive control applied to a supply chain management problem

Supply chains are ubiquitous in any commercial delivery systems. The exchange of goods and services, from different supply points to distinct destinations scattered along a given geographical area, requires the management of stocks and vehicles fleets in order to minimize costs while maintaining good quality services. Even if the operating conditions remain constant over a given time horizon, managing a supply chain is a very complex task. Its complexity increases exponentially with both the number of network nodes and the dynamical operational changes. Moreover, the management system must be adaptive in order to easily cope with several disturbances such as machinery and vehicles breakdowns or changes in demand. This work proposes the use of a model predictive control paradigm in order to tackle the above referred issues. The obtained simulation results suggest that this strategy promotes an easy tasks rescheduling in case of disturbances or anticipated changes in operating conditions. © Springer International Publishing Switzerland 2017

Analysis of distributed beamforming in cooperative communications network with phase shifter based smart antenna nodes

This research study investigates the application of phase shifter-based smart antenna system in distributed beamforming. It examines the way to optimise the transmit power by jointly maximising the directivity of the array antennas and the weight vector for distributed beamforming. This research study concludes that maximising directivity can lead to better transmit power minimisation compared to maximising field intensity. This study also concludes that signal to noise power ratio maximisation subject to a power constraint and power minimisation subject to a signal to noise power ratio constraint yield the same results.

Model Based Target Tracking in a Wireless Network of Passive Infrared Sensor Nodes

We consider the problem of tracking an intruder in a plane region by using a wireless sensor network comprising motes equipped with passive infrared (PIR) sensors deployed over the region. An input-output model for the PIR sensor and a method to estimate the angular speed of the target from the sensor output are proposed. With the measurement model so obtained, we study the centralized and decentralized tracking performance using the extended Kalman filter.

THE MATRIX ANALYSIS FOR AN OPTICAL FREE-SPACE SWITCHING NETWORK AND AN OPTICAL CROSSOVER NETWORK WITH 4-FUNCTION INTERCHANGE NODES

A matrix analysis for free-space switching networks, such as perfect shuffle-exchange omega, crossover and Banyan is presented. On the basis of matrix analysis, the equivalence of these three switching networks and the route selection between input and output ports are simply explained. Furthermore, an optical crossover switching network, where MQW SEED arrays are used as electrically addressed four-function interchange nodes, is described and the optical crossover interconnection of 64 x 64, and high-speed four-function, interchange nodes is demonstrated in the experiment.

Evaluating the impact of network density, hidden nodes and capture effect for throughput guarantee in multi-hop wireless networks

To optimize the performance of wireless networks, one needs to consider the impact of key factors such as interference from hidden nodes, the capture effect, the network density and network conditions (saturated versus non-saturated). In this research, our goal is to quantify the impact of these factors and to propose effective mechanisms and algorithms for throughput guarantees in multi-hop wireless networks. For this purpose, we have developed a model that takes into account all these key factors, based on which an admission control algorithm and an end-to-end available bandwidth estimation algorithm are proposed. Given the necessary network information and traffic demands as inputs, these algorithms are able to provide predictive control via an iterative approach. Evaluations using analytical comparison with simulations as well as existing research show that the proposed model and algorithms are accurate and effective.

Trapping of naive lymphocytes triggers rapid growth and remodeling of the fibroblast network in reactive murine lymph nodes.

Adaptive immunity is initiated in T-cell zones of secondary lymphoid organs. These zones are organized in a rigid 3D network of fibroblastic reticular cells (FRCs) that are a rich cytokine source. In response to lymph-borne antigens, draining lymph nodes (LNs) expand several folds in size, but the fate and role of the FRC network during immune response is not fully understood. Here we show that T-cell responses are accompanied by the rapid activation and growth of FRCs, leading to an expanded but similarly organized network of T-zone FRCs that maintains its vital function for lymphocyte trafficking and survival. In addition, new FRC-rich environments were observed in the expanded medullary cords. FRCs are activated within hours after the onset of inflammation in the periphery. Surprisingly, FRC expansion depends mainly on trapping of naïve lymphocytes that is induced by both migratory and resident dendritic cells. Inflammatory signals are not required as homeostatic T-cell proliferation was sufficient to trigger FRC expansion. Activated lymphocytes are also dispensable for this process, but can enhance the later growth phase. Thus, this study documents the surprising plasticity as well as the complex regulation of FRC networks allowing the rapid LN hyperplasia that is critical for mounting efficient adaptive immunity.

Orthogonal forward selection for constructing the radial basis function network with tunable nodes

An orthogonal forward selection (OFS) algorithm based on the leave-one-out (LOO) criterion is proposed for the construction of radial basis function (RBF) networks with tunable nodes. This OFS-LOO algorithm is computationally efficient and is capable of identifying parsimonious RBF networks that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process.

Cattle-powered nodes experience in a heterogeneous network for localization of herds

A heterogeneous network, mainly based on nodes that use harvested energy to self-energize is presented and its use demonstrated. The network, mostly kinetically powered, has been used for the localization of herds in grazing areas under extreme climate conditions. The network consists of secondary and primary nodes. The former, powered by a kinetic generator, take advantage of animal movements to broadcast a unique identifier. The latter are battery-powered and gather secondarynode transmitted information to provide it, along with position and time data, to a final base station in charge of the animal monitoring. Because a limited human interaction is desirable, the aim of this network is to reduce the battery count of the system.

Spatial network surrogates for disentangling complex system structure from spatial embedding of nodes

ACKNOWLEDGMENTS MW and RVD have been supported by the German Federal Ministry for Education and Research (BMBF) via the Young Investigators Group CoSy-CC2 (grant no. 01LN1306A). JFD thanks the Stordalen Foundation and BMBF (project GLUES) for financial support. JK acknowledges the IRTG 1740 funded by DFG and FAPESP. MT Gastner is acknowledged for providing his data on the airline, interstate, and Internet network. P Menck thankfully provided his data on the Scandinavian power grid. We thank S Willner on behalf of the entire zeean team for providing the data on the world trade network. All computations have been performed using the Python package pyunicorn [41] that is available at https://github.com/pik-copan/pyunicorn.