A duality based framework for integrating reliability and precision for sensor network design

In sensor network design literature, requirements such as maximization of the network reliability [Y. Ali, S. Narasimhan, Sensor network design for maximizing reliability of linear processes, AIChE J. 39 (1993) 820–828; Y. Ali, S. Narasimhan, Redundant sensor network design for linear processes, AIChE J. 41 (1995) 2237–2249] and minimization of cost subject to precision constraints [M. Bagajewicz, Design and retrofit of sensor networks in process plants, AIChE J. 43 (1997) 2300–2306; M. Bagajewicz, E. Cabrera, New MILP formulation for instrumentation network design and upgrade, AIChE J. 48 (2002) 2271–2282] have been proposed as a criteria for optimally locating sensors. In this article, we show that the problems of maximizing reliability and maximizing precision (or minimizing variance) for linear processes are dual of each other. To achieve this duality, we propose transformations which can be used to convert sensor failure probabilities into equivalent sensor variances and vice versa. Thus, the duality enables working in a single framework with specified criteria on reliability as well as precision. As an application of this duality, we propose two formulations for the sensor network design problem viz., maximization of the network reliability subject to precision constraints and minimization of the network variance subject to reliability constraints. We also show the utility of these formulations to determine the pareto-front for the combinatorial sensor network design problem. Hydrodealkylation and steam-metering case studies are used to illustrate the proposed ideas.     

Efficient optimization strategies with constraint programming

In this article, we propose novel strategies for the efficient determination of multiple solutions for a single objective, as well as globally optimal pareto fronts for multiobjective, optimization problems using Constraint Programming (CP). In particular, we propose strategies to determine, (i) all the multiple (globally) optimal solutions of a single objective optimization problem, (ii) K‐best feasible solutions of a single objective optimization problem, and (iii) globally optimal pareto fronts (including nonconvex pareto fronts) along with their multiple realizations for multiobjective optimization problems. It is shown here that the proposed strategy for determining K‐best feasible solutions can be tuned as per the requirement of the user to determine either K‐best distinct or nondistinct solutions. Similarly, the strategy for determining globally optimal pareto fronts can also be modified as per the requirement of the user to determine either only the distinct set of pareto points or determine the pareto points along with all their multiple realizations. All the proposed techniques involve appropriately modifying the search techniques and are shown to be computationally efficient in terms of not requiring successive re‐solving of the problem to obtain the required solutions. This work therefore convincingly addresses the issue of efficiently determining globally optimal pareto fronts; in addition, it also guarantees the determination of all the possible realizations associated with each pareto point. The uncovering of such solutions can greatly aid the designer in making informed decisions. The proposed approaches are demonstrated via two case studies, which are nonlinear, combinatorial optimization problems, taken from the area of sensor network design. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Soluble L-selectin concentration in bronchoalveolar lavage fluid obtained from infants who develop chronic lung disease of prematurity

AIMS: To explore the changes in neutrophil adhesion molecule expression and release into bronchoalveolar lavage fluid (BAL) obtained from infants who developed chronic lung disease (CLD). METHODS: BAL fluid was obtained from 37 infants: 18 (median gestation 26 weeks, birthweight 835 g) who developed CLD, 12 (29 weeks, 1345 g) with respiratory distress syndrome (RDS) and seven control infants (33 weeks, 2190 g). RESULTS: Soluble L-selectin (sL-selectin) in BAL fluid from the CLD and non-CLD groups was similar immediately after birth, but in infants who subsequently developed CLD, sL-selectin remained persistently increased (at day 7: CLD 42.6 vs RDS 6.0 ng/ml, p < 0.05; CLD vs controls 1.5 ng/ml; p < 0.05). CD11b/CD18 expression on neutrophils obtained by BAL increased with time to reach a maximum at 17 days of age in infants who developed CLD. CONCLUSIONS: These results suggest that leucocyte traffic persists in infants who develop CLD and may have an important part to play in the pathogenesis of CLD.     

Distributed multitarget classification in wireless sensor networks

We study distributed strategies for classification of multiple targets in a wireless sensor network. The maximum number of targets is known a priori but the actual number of distinct targets present in any given event is assumed unknown. The target signals are modeled as zero-mean Gaussian processes with distinct temporal power spectral densities, and it is assumed that the noise-corrupted node measurements are spatially independent. The proposed classifiers have a simple distributed architecture: local hard decisions from each node are communicated over noisy links to a manager node which optimally fuses them to make the final decision. A natural strategy for local hard decisions is to use the optimal local classifier. A key problem with the optimal local classifier is that the number of hypotheses increases exponentially with the maximum number of targets. We propose two suboptimal (mixture density and Gaussian) local classifiers that are based on a natural but coarser repartitioning of the hypothesis space, resulting in linear complexity with the number of targets. We show that exponentially decreasing probability of error with the number of nodes can be guaranteed with an arbitrarily small but nonvanishing communication power per node. Numerical results based on real data demonstrate the remarkable practical advantage of decision fusion: an acceptably small probability of error can be attained by fusing a moderate number of unreliable local decisions. Furthermore, the performance of the suboptimal mixture density classifier is comparable to that of the optimal local classifier, making it an attractive choice in practice.     

Construction of linear time‐invariant equivalent plants for nonlinear continuous‐time and sampled‐data systems

In this paper, a technique is presented for constructing the so‐called linear time‐invariant equivalent (LTIE) plants used in the nonlinear quantitative feed back theory (QFT) approach. The proposed technique is applicable equally well to both continuous‐time and sampled‐data nonlinear plants described by nonlinear integro‐differential equation and nonlinear autoregressive moving average with exogenous inputs (NARMAX) models, respectively. The technique uses inclusion function and interval integration tools of interval analysis. A nonlinear chemical reactor example is used to illustrate the procedure for both nonlinear continuous‐time and sampled‐data systems, and found to yield satisfactory results in both cases. Copyright © 2003 John Wiley & Sons, Ltd.     

Monitoring Gut Epithelium Serotonin and Melatonin Overflow Provides Spatial Mapping of Inflammation

Electrochemical arrays were used to measure the overflow of serotonin (5-HT) and melatonin (MEL) from the entire colon of healthy mice and mice with chemical-induced inflammatory bowel disease (IBD), to understand the interplay between inflammation and colonic function. We show that 5-HT overflow is increased, whilst MEL levels are reduced, in inflamed tissues. The levels of MEL are increased at the interface between healthy and inflamed regions within the colon and may limit the spread of inflammation. Understanding the interplay between inflammation and mucosal epithelial signalling can provide key insight into colonic function and aid the development of effective therapeutic strategies to treat gastrointestinal diseases.     

An Evolutionary Normalization Algorithm for Signed Floating-Point Multiply-Accumulate Operation

In the era of digital signal processing, like graphics and computation systems, multiplication-accumulation is one of the prime operations. A MAC unit is a vital component of a digital system, like different Fast Fourier Transform (FFT) algorithms, convolution, image processing algorithms, etcetera. In the domain of digital signal processing, the use of normalization architecture is very vast. The main objective of using normalization is to perform comparison and shift operations. In this research paper, an evolutionary approach for designing an optimized normalization algorithm is proposed using basic logical blocks such as Multiplexer, Adder etc. The proposed normalization algorithm is further used in designing an 8 × 8 bit Signed Floating-Point Multiply-Accumulate (SFMAC) architecture. Since the SFMAC can accept an 8-bit significand and a 3-bit exponent, the input to thesaid architecture can be somewhere between −(7.96872)₁₀ to + (7.96872)₁₀. The proposed architecture is designed and implemented using the Cadence Virtuoso using 90 and 130 nm technologies (in Generic Process Design Kit (GPDK) and Taiwan Semiconductor Manufacturing Company (TSMC), respectively). To reduce the power consumption of the proposed normalization architecture, techniques such as “block enabling” and “clock gating” are used rigorously. According to the analysis done on Cadence, the proposed architecture uses the least amount of power compared to its current predecessors.     

Exergetic optimization of solar water collectors using computational intelligence techniques

Clean Technologies and Environmental Policy - This article proposes a model to determine the optimal performance and design conditions for a flat plate solar water collector. The model uses the...     

Gaussian particle filtering

Sequential Bayesian estimation for nonlinear dynamic state-space models involves recursive estimation of filtering and predictive distributions of unobserved time varying signals based on noisy observations. This paper introduces a new filter called the Gaussian particle filter. It is based on the particle filtering concept, and it approximates the posterior distributions by single Gaussians, similar to Gaussian filters like the extended Kalman filter and its variants. It is shown that under the Gaussianity assumption, the Gaussian particle filter is asymptotically optimal in the number of particles and, hence, has much-improved performance and versatility over other Gaussian filters, especially when nontrivial nonlinearities are present. Simulation results are presented to demonstrate the versatility and improved performance of the Gaussian particle filter over conventional Gaussian filters and the lower complexity than known particle filters.     

Global Optimization with Higher Order Inclusion Function Forms Part 1: A Combined Taylor-Bernstein Form

Recently, two versions of the so-called Taylor-Bernstein (TB) form having the property of higher order convergence were proposed in [5] and [10]. However, in many application problems, both these TB forms encounter difficulties in computing the range enclosures for some domain widths, due to excessive memory and/or time requirements. In this paper, we present a combined TB form that is more successful in computing the range enclosures as the domain shrinks from large to small widths. We test and compare the performance of the proposed form with those of the existing TB forms, the Taylor model of Berz et al. [1], and the simple natural inclusion function form. For the testing, we consider six benchmark examples with dimensions varying from 1 to 6. The results of the tests show that the proposed combined TB form is indeed more effective than the existing TB forms and the Taylor model, over the entire range of domain widths considered. This revised version was published online in June 2006 with corrections to the Cover Date.