Multicode MIMO for High Data Rate Mobile Ad-hoc Networks

In a mobile ad-hoc network scenario, where communication nodes are mounted on moving platforms (like jeeps, trucks, tanks, etc.), use of V-BLAST requires that the number of receive antennas in a given node must be greater than or equal to the sum of the number of transmit antennas of all its neighbor nodes. This limits the achievable spatial multiplexing gain (data rate) for a given node. In such a scenario, we propose to achieve high data rates per node through multicode direct sequence spread spectrum techniques in conjunction with V-BLAST. In the considered multicode V-BLAST system, the receiver experiences code domain interference (CDI) in frequency selective fading, in addition to space domain interference (SDI) experienced in conventional V-BLAST systems. We propose two interference cancelling receivers that employ a linear parallel interference cancellation approach to handle the CDI, followed by conventional V-BLAST detector to handle the SDI, and then evaluate their bit error rates.

Robust/Optimal Temperature Profile Control of a High-Speed Aerospace Vehicle Using Neural Networks

An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A I-D distributed parameter model of a fin is developed from basic thermal physics principles. "Snapshot" solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the "proper orthogonal decomposition" (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.

Cell versus message level performances in ATM networks

The goal of this paper is to provide some insight into the relations that exist between cell level and message level performance guarantees in the context of ATM networks. Cell level guarantees are typically what the network is capable of providing, while message level guarantees are the ones of interest to users. It is, therefore, important to understand how the two are related, and which factors influence this relation. There are many different performance measures that are of importance, and in this paper we try to touch on the (three) most relevant ones. This includes comparing cell and message loss probabilities, average cell and message delays, and cell and message jitter. Specifically, we show that cell and message loss probabilities can exhibit significant differences, which strongly depend on traffic characteristics such as peak rate and burst size, i.e., for a fixed cell loss probability, the message loss probability can greatly vary when peak rate and burst size change. One reason for this sensitivity, is that message loss depends on what happen to all the cells in a message. For delay and jitter, we also find that peak rate and burst size play a role in determining the relation between cell and message performance. However, this sensitivity is not as acute as with losses since message delay and jitter are typically determined by the performance seen by only one cell, the last cell in a message. In the paper, we provide quantitative examples that illustrate the range of behaviors and identify the impact of different parameters.

Design of logical topologies for wavelength-routed optical networks

his paper studies the problem of designing a logical topology over a wavelength-routed all-optical network (AON) physical topology, The physical topology consists of the nodes and fiber links in the network, On an AON physical topology, we can set up lightpaths between pairs of nodes, where a lightpath represents a direct optical connection without any intermediate electronics, The set of lightpaths along with the nodes constitutes the logical topology, For a given network physical topology and traffic pattern (relative traffic distribution among the source-destination pairs), our objective is to design the logical topology and the routing algorithm on that topology so as to minimize the network congestion while constraining the average delay seen by a source-destination pair and the amount of processing required at the nodes (degree of the logical topology), We will see that ignoring the delay constraints can result in fairly convoluted logical topologies with very long delays, On the other hand, in all our examples, imposing it results in a minimal increase in congestion, While the number of wavelengths required to imbed the resulting logical topology on the physical all optical topology is also a constraint in general, we find that in many cases of interest this number can be quite small, We formulate the combined logical topology design and routing problem described above (ignoring the constraint on the number of available wavelengths) as a mixed integer linear programming problem which we then solve for a number of cases of a six-node network, Since this programming problem is computationally intractable for larger networks, we split it into two subproblems: logical topology design, which is computationally hard and will probably require heuristic algorithms, and routing, which can be solved by a linear program, We then compare the performance of several heuristic topology design algorithms (that do take wavelength assignment constraints into account) against that of randomly generated topologies, as well as lower bounds derived in the paper.

Generation of novel materials for nonlinear optics: Analysis of hydrogen-bonded networks in haloaromatic and aliphatic amine-tartrate complexes

Five tartrate-amine complexes have been studied in terms of crystal packing and hydrogen bonding frameworks. The salts are 3-bromoanilinium-L-monohydrogen tartrate 1, 3-fluoroanilinium-D-dibenzoylmonohydrogen tartrate 2, 1-nonylium-D-dibenzoylmonohydrogen tartrate 3, 1 -decylium-D-dibenzoylmonohydrogen tartrate 4, and 1,4-diaminobutanium-D-dibenzoyl tartrate trihydrate 5. The results indicate that there are no halogen-halogen interactions in the haloaromatic-tartrate complexes. The anionic framework allows accomodation of ammonium ions that bear alkyl chain residues of variable lengths. The long chain amines in these structures remain disordered while the short chain amines form multidirectional hydrogen bonds on either side.

Pipelined ring algorithm for matrix multiplication on transputer networks: performance analysis and estimation

A parallel matrix multiplication algorithm is presented, and studies of its performance and estimation are discussed. The algorithm is implemented on a network of transputers connected in a ring topology. An efficient scheme for partitioning the input matrices is introduced which enables overlapping computation with communication. This makes the algorithm achieve near-ideal speed-up for reasonably large matrices. Analytical expressions for the execution time of the algorithm have been derived by analysing its computation and communication characteristics. These expressions are validated by comparing the theoretical results of the performance with the experimental values obtained on a four-transputer network for both square and irregular matrices. The analytical model is also used to estimate the performance of the algorithm for a varying number of transputers and varying problem sizes. Although the algorithm is implemented on transputers, the methodology and the partitioning scheme presented in this paper are quite general and can be implemented on other processors which have the capability of overlapping computation with communication. The equations for performance prediction can also be extended to other multiprocessor systems.

Synthesis and mesogenic behavior of metal-containing liquid crystalline networks

A new hydroxy functionalized liquid crystalline (LC) polyazomethine has been synthesized by the solution polycondensation of a dialdehyde with a diamine. The polymer was characterized by IR, H-1-, and C-13-NMR spectroscopy. Studies on the liquid crystalline properties reveal the nematic mesomorphic behavior. This polymer functions as a polymeric chelate and forms a three-dimensional network structure through the metal complexation. Influence of various metals and their concentration on the liquid crystalline behavior of the network has been studied. Networks up to 30 mol % of the metal show LC phase transitions; above this the transitions are suppressed and the network behaves like an LC thermoset. (C) 1996 John Wiley & Sons, Inc.

Systematic crystallographic investigation of hydrogen-bonded networks involving monohydrogen tartrate-amine complexes: Potential materials for nonlinear optics

Crystal structures of six binary salts involving aromatic amines as cations and hydrogen tartrates as anions are presented. The materials are 2,6-xylidinium-L-monohydrogen tartrate monohydrate, C12H18O6.5N, P22(1)2(1), a = 7.283(2) Angstrom, b = 17.030(2) Angstrom, c = 22.196(2) Angstrom, Z = 8; 2,6-xylidinium-D-dibenzoyl monohydrogen tartrate, C26H25O8N, P2(1), a = 7.906(1) Angstrom, b = 24.757(1) Angstrom, c = 13.166(1) Angstrom, beta = 105.01(1)degrees, Z = 4; 2,3-xylidinium-D-dibenzoyl monohydrogen tartrate monohydrate, C26H26O8.5N, P2(1), a = 7.837(1) Angstrom, b = 24.488(1) Angstrom, c = 13.763(1) Angstrom, beta = 105.69(1)degrees, Z = 4; 2-toluidinium-D-dibenzoyl monohydrogen tartrate, C25H23O8N, P2(1)2(1)2(1), a = 13.553(2) Angstrom, b = 15.869(3) Angstrom, c = 22.123(2) Angstrom, Z = 8; 3-toluidinium-D-dibenzoyl monohydrogen tartrate (1:1), C25H23O8N, P1, a = 7.916(3) Angstrom, b = 11.467(6) Angstrom, c = 14.203(8) Angstrom, alpha = 96.44(4)degrees, beta = 98.20(5)degrees, = 110.55(5)degrees, Z = 2; 3-toluidinium-D-dibenzoyl tartrate dihydrate (1:2), C32H36O10N, P1, a = 7.828(3) Angstrom, b = 8.233(1) Angstrom, c = 24.888(8) Angstrom, alpha = 93.98 degrees, beta = 94.58(3)degrees, = 89.99(2)degrees, Z = 2. An analysis of the hydrogen-bonding schemes in terms of crystal packing, stoichiometric variations, and substitutional variations in these materials provides insights to design hydrogen-bonded networks directed toward the engineering of crystalline nonlinear optical materials.

WLS method for parameter estimation in water distribution networks

The weighted-least-squares method based on the Gauss-Newton minimization technique is used for parameter estimation in water distribution networks. The parameters considered are: element resistances (single and/or group resistances, Hazen-Williams coefficients, pump specifications) and consumptions (for single or multiple loading conditions). The measurements considered are: nodal pressure heads, pipe flows, head loss in pipes, and consumptions/inflows. An important feature of the study is a detailed consideration of the influence of different choice of weights on parameter estimation, for error-free data, noisy data, and noisy data which include bad data. The method is applied to three different networks including a real-life problem.

Link failure detection for maintaining session continuity in packet data networks

A link failure in the path of a virtual circuit in a packet data network will lead to premature disconnection of the circuit by the end-points. A soft failure will result in degraded throughput over the virtual circuit. If these failures can be detected quickly and reliably, then appropriate rerouteing strategies can automatically reroute the virtual circuits that use the failed facility. In this paper, we develop a methodology for analysing and designing failure detection schemes for digital facilities. Based on errored second data, we develop a Markov model for the error and failure behaviour of a T1 trunk. The performance of a detection scheme is characterized by its false alarm probability and the detection delay. Using the Markov model, we analyse the performance of detection schemes that use physical layer or link layer information. The schemes basically rely upon detecting the occurrence of severely errored seconds (SESs). A failure is declared when a counter, that is driven by the occurrence of SESs, reaches a certain threshold.For hard failures, the design problem reduces to a proper choice;of the threshold at which failure is declared, and on the connection reattempt parameters of the virtual circuit end-point session recovery procedures. For soft failures, the performance of a detection scheme depends, in addition, on how long and how frequent the error bursts are in a given failure mode. We also propose and analyse a novel Level 2 detection scheme that relies only upon anomalies observable at Level 2, i.e. CRC failures and idle-fill flag errors. Our results suggest that Level 2 schemes that perform as well as Level 1 schemes are possible.

Mathematical modelling of neurons and neural networks

The basic concepts and techniques involved in the development and analysis of mathematical models for individual neurons and networks of neurons are reviewed. Some of the interesting results obtained from recent work in this field are described. The current status of research in this field in India is discussed

Multi-installment load distribution in tree networks with delays

This paper presents a new strategy for load distribution in a single-level tree network equipped with or without front-ends. The load is distributed in more than one installment in an optimal manner to minimize the processing time. This is a deviation and an improvement over earlier studies in which the load distribution is done in only one installment. Recursive equations for the general case, and their closed form solutions for a special case in which the network has identical processors and identical links, are derived. An asymptotic analysis of the network performance with respect to the number of processors and the number of installments is carried out. Discussions of the results in terms of some practical issues like the tradeoff relationship between the number of processors and the number of installments are also presented.

Vibration Signature Analysis Using Artificial Neural Networks

Damage detection by measuring and analyzing vibration signals in a machine component is an established procedure in mechanical and aerospace engineering. This paper presents vibration signature analysis of steel bridge structures in a nonconventional way using artificial neural networks (ANN). Multilayer perceptrons have been adopted using the back-propagation algorithm for network training. The training patterns in terms of vibration signature are generated analytically for a moving load traveling on a trussed bridge structure at a constant speed to simulate the inspection vehicle. Using the finite-element technique, the moving forces are converted into stationary time-dependent force functions in order to generate vibration signals in the structure and the same is used to train the network. The performance of the trained networks is examined for their capability to detect damage from unknown signatures taken independently at one, three, and five nodes. It has been observed that the prediction using the trained network with single-node signature measurement at a suitability chosen location is even better than that of three-node and five-node measurement data.

The actor-critic algorithm as multi-time-scale stochastic approximation

The actor-critic algorithm of Barto and others for simulation-based optimization of Markov decision processes is cast as a two time Scale stochastic approximation. Convergence analysis, approximation issues and an example are studied.