Optimal sleep-wake scheduling for quickest intrusion detection using sensor networks

We consider the problem of quickest detection of an intrusion using a sensor network, keeping only a minimal number of sensors active. By using a minimal number of sensor devices, we ensure that the energy expenditure for sensing, computation and communication is minimized (and the lifetime of the network is maximized). We model the intrusion detection (or change detection) problem as a Markov decision process (MDP). Based on the theory of MDP, we develop the following closed loop sleep/wake scheduling algorithms: (1) optimal control of Mk+1, the number of sensors in the wake state in time slot k + 1, (2) optimal control of qk+1, the probability of a sensor in the wake state in time slot k + 1, and an open loop sleep/wake scheduling algorithm which (3) computes q, the optimal probability of a sensor in the wake state (which does not vary with time), based on the sensor observations obtained until time slot k. Our results show that an optimum closed loop control on Mk+1 significantly decreases the cost compared to keeping any number of sensors active all the time. Also, among the three algorithms described, we observe that the total cost is minimum for the optimum control on Mk+1 and is maximum for the optimum open loop control on q.

Optimal configurations of active fiber composites based on asymptotic torsional analysis - art. no. 641413

Active Fiber Composites (AFC) possess desirable characteristics over a wide range of smart structure applications, such as vibration, shape and flow control as well as structural health monitoring. This type of material, capable of collocated actuation and sensing, call be used in smart structures with self-sensing circuits. This paper proposes four novel applications of AFC structures undergoing torsion: sensors and actuators shaped as strips and tubes; and concludes with a preliminary failure analysis. To enable this, a powerful mathematical technique, the Variational Asymptotic Method (VAM) was used to perform cross-sectional analyses of thin generally anisotropic AFC beams. The resulting closed form expressions have been utilized in the applications presented herein.

Optimal auctions for multi-unit procurement with volume discount bids

Our attention, is focused on designing an optimal procurement mechanism which a buyer can use for procuring multiple units of a homogeneous item based on bids submitted by autonomous, rational, and intelligent suppliers. We design elegant optimal procurement mechanisms for two different situations. In the first situation, each supplier specifies the maximum quantity that can be supplied together with a per unit price. For this situation, we design an optimal mechanism S-OPT (Optimal with Simple bids). In the more generalized case, each supplier specifies discounts based on the volume of supply. In this case, we design an optimal mechanism VD-OPT (Optimal with Volume Discount, bids). The VD-OPT mechanism uses the S-OPT mechanism as a building block. The proposed mechanisms minimize the cost to the buyer, satisfying at the same time, (a) Bayesian, incentive compatibility and (b) interim individual rationality.

Evolution of domain combinations in protein kinases and its implications for functional diversity

Protein kinases phosphorylating Ser/Thr/Tyr residues in several cellular proteins exert tight control over their biological functions. They constitute the largest protein family in most eukaryotic species. Protein kinases classified based on sequence similarity in their catalytic domains, cluster into subfamilies, which share gross functional properties. Many protein kinases are associated or tethered covalently to domains that serve as adapter or regulatory modules,naiding substrate recruitment, specificity, and also serve as scaffolds. Hence the modular organisation of the protein kinases serves as guidelines to their functional and molecular properties. Analysis of genomic repertoires of protein kinases in eukaryotes have revealed wide spectrum of domain organisation across various subfamilies of kinases. Occurrence of organism-specific novel domain combinations suggests functional diversity achieved by protein kinases in order to regulate variety of biological processes. In addition, domain architecture of protein kinases revealed existence of hybrid protein kinase subfamilies and their emerging roles in the signaling of eukaryotic organisms. In this review we discuss the repertoire of non-kinase domains tethered to multi-domain kinases in the metazoans. Similarities and differences in the domain architectures of protein kinases in these organisms indicate conserved and unique features that are critical to functional specialization. (C) 2009 Elsevier Ltd. All rights reserved.

A unified construction of space-time codes with optimal rate-diversity tradeoff

The problem of constructing space-time (ST) block codes over a fixed, desired signal constellation is considered. In this situation, there is a tradeoff between the transmission rate as measured in constellation symbols per channel use and the transmit diversity gain achieved by the code. The transmit diversity is a measure of the rate of polynomial decay of pairwise error probability of the code with increase in the signal-to-noise ratio (SNR). In the setting of a quasi-static channel model, let n(t) denote the number of transmit antennas and T the block interval. For any n(t) <= T, a unified construction of (n(t) x T) ST codes is provided here, for a class of signal constellations that includes the familiar pulse-amplitude (PAM), quadrature-amplitude (QAM), and 2(K)-ary phase-shift-keying (PSK) modulations as special cases. The construction is optimal as measured by the rate-diversity tradeoff and can achieve any given integer point on the rate-diversity tradeoff curve. An estimate of the coding gain realized is given. Other results presented here include i) an extension of the optimal unified construction to the multiple fading block case, ii) a version of the optimal unified construction in which the underlying binary block codes are replaced by trellis codes, iii) the providing of a linear dispersion form for the underlying binary block codes, iv) a Gray-mapped version of the unified construction, and v) a generalization of construction of the S-ary case corresponding to constellations of size S-K. Items ii) and iii) are aimed at simplifying the decoding of this class of ST codes.

Optimal multicommodity flow through the complete graph with random edge capacities

We consider a multicommodity flow problem on a complete graph whose edges have random, independent, and identically distributed capacities. We show that, as the number of nodes tends to infinity, the maximumutility, given by the average of a concave function of each commodity How, has an almost-sure limit. Furthermore, the asymptotically optimal flow uses only direct and two-hop paths, and can be obtained in a distributed manner.

Interactions of linear dicationic molecules with lipid A: structural requisites for optimal binding affinity

The structural determinants of the binding affinity of linear dicationic molecules toward lipid A have been examined with respect to the distance between the terminal cationic functions, the basicity, and the type of cationic moieties using a series of spermidine derivatives and pentamidine analogs by fluorescence spectroscopic methods, The presence of two terminal cationic groups corresponds to enhanced affinity, A distinct sigmoidal relationship between the intercationic distance and affinity was observed with a sharp increase at 11 Angstrom, levelling off at about 13 Angstrom. The basicity (pK) and nature of the cationic functions are poor correlates of binding potency, since molecules bearing primary amino, imidazolino, or guanido termini are equipotent, The interaction of pentamidine, a bisamidine drug, with lipid A, characterized in considerable detail employing the putative intermolecular excimerization of the drug, suggests a stoichiometry of 1:1 in the resultant complex, The binding is driven almost exclusively by electrostatic forces, and is dependent on the ionization states of both lipid A and the drug, Under conditions when lipid A is highly disaggregated, pentamidine binds specifically to bis-phosphoryl- but not to monophosphoryl-lipid A indicating that both phosphate groups of lipid A are necessary for electrostatic interactions by the terminal amidininium groups of the drug, Based on these data, a structural model is proposed for the pentamidine-lipid A complex, which may be of value in designing endotoxin antagonists from first principles.

Optimal Resource Partitioning in a Military Conflict based on Lanchester Attrition Models

This paper develops a model for military conflicts where the defending forces have to determine an optimal partitioning of available resources to counter attacks from an adversary in two different fronts. The Lanchester attrition model is used to develop the dynamical equations governing the variation in force strength. Three different allocation schemes - Time-Zero-Allocation (TZA), Allocate-Assess-Reallocate (AAR), and Continuous Constant Allocation (CCA) - are considered and the optimal solutions are obtained in each case. Numerical examples are given to support the analytical results.

Delay optimal control algorithm for a multiaccess fading channel with peak power constraint

We consider an optimal power and rate scheduling problem for a multiaccess fading wireless channel with the objective of minimising a weighted sum of mean packet transmission delay subject to a peak power constraint. The base station acts as a controller which, depending upon the buffer lengths and the channel state of each user, allocates transmission rate and power to individual users. We assume perfect channel state information at the transmitter and the receiver. We also assume a Markov model for the fading and packet arrival processes. The policy obtained represents a form of Indexability.

Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm

Many optimal control problems are characterized by their multiple performance measures that are often noncommensurable and competing with each other. The presence of multiple objectives in a problem usually give rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Evolutionary algorithms have been recognized to be well suited for multi-objective optimization because of their capability to evolve a set of nondominated solutions distributed along the Pareto front. This has led to the development of many evolutionary multi-objective optimization algorithms among which Nondominated Sorting Genetic Algorithm (NSGA and its enhanced version NSGA-II) has been found effective in solving a wide variety of problems. Recently, we reported a genetic algorithm based technique for solving dynamic single-objective optimization problems, with single as well as multiple control variables, that appear in fed-batch bioreactor applications. The purpose of this study is to extend this methodology for solution of multi-objective optimal control problems under the framework of NSGA-II. The applicability of the technique is illustrated by solving two optimal control problems, taken from literature, which have usually been solved by several methods as single-objective dynamic optimization problems. (C) 2004 Elsevier Ltd. All rights reserved.

Optimal Resource Partitioning in a Military Conflict based on Lanchester Attrition Models

This paper develops a model for military conflicts where the defending forces have to determine an optimal partitioning of available resources to counter attacks from an adversary in two different fronts. The Lanchester attrition model is used to develop the dynamical equations governing the variation in force strength. Three different allocation schemes - Time-Zero-Allocation (TZA), Allocate-Assess-Reallocate (AAR), and Continuous Constant Allocation (CCA) - are considered and the optimal solutions are obtained in each case. Numerical examples are given to support the analytical results.

Optimal threshold policies for admission control in communication networks via discrete parameter stochastic approximation

The problem of admission control of packets in communication networks is studied in the continuous time queueing framework under different classes of service and delayed information feedback. We develop and use a variant of a simulation based two timescale simultaneous perturbation stochastic approximation (SPSA) algorithm for finding an optimal feedback policy within the class of threshold type policies. Even though SPSA has originally been designed for continuous parameter optimization, its variant for the discrete parameter case is seen to work well. We give a proof of the hypothesis needed to show convergence of the algorithm on our setting along with a sketch of the convergence analysis. Extensive numerical experiments with the algorithm are illustrated for different parameter specifications. In particular, we study the effect of feedback delays on the system performance.

Power Optimal Network-on-Chip Interconnect Design

A large part of today's multi-core chips is interconnect. Increasing communication complexity has made essential new strategies for interconnects, such as Network on Chip. Power dissipation in interconnects has become a substantial part of the total power dissipation. Techniques to reduce interconnect power have thus become a necessity. In this paper, we present a design methodology that gives values of bus width for interconnect links, frequency of operation for routers, in Network on Chip scenario that satisfy required throughput and dissipate minimal switching power. We develop closed form analytical expressions for the power dissipation, with bus width and frequency as variables and then use Lagrange multiplier method to arrive at the optimal values. We present a 4 port router in 90 nm technology library as case study. The results obtained from analysis are discussed.

Optimal non-linear reinforcement schemes for stochastic automata

Two optimal non-linear reinforcement schemes—the Reward-Inaction and the Penalty-Inaction—for the two-state automaton functioning in a stationary random environment are considered. Very simple conditions of symmetry of the non-linear function figuring in the reinforcement scheme are shown to be necessary and sufficient for optimality. General expressions for the variance and rate of learning are derived. These schemes are compared with the already existing optimal linear schemes in the light of average variance and average rate of learning.

Optimal design of multiproduct batch chemical plant using NSGA-II

The optimal design of a multiproduct batch chemical plant is formulated as a multiobjective optimization problem, and the resulting constrained mixed-integer nonlinear program (MINLP) is solved by the nondominated sorting genetic algorithm approach (NSGA-II). By putting bounds on the objective function values, the constrained MINLP problem can be solved efficiently by NSGA-II to generate a set of feasible nondominated solutions in the range desired by the decision-maker in a single run of the algorithm. The evolution of the entire set of nondominated solutions helps the decision-maker to make a better choice of the appropriate design from among several alternatives. The large set of solutions also provides a rich source of excellent initial guesses for solution of the same problem by alternative approaches to achieve any specific target for the objective functions