Analysis of autocatalytic networks in biology

Autocatalytic networks, in particular the glycolytic pathway, constitute an important part of the cell metabolism. Changes in the concentration of metabolites and catalyzing enzymes during the lifetime of the cell can lead to perturbations from its nominal operating condition. We investigate the effects of such perturbations on stability properties, e.g., the extent of regions of attraction, of a particular family of autocatalytic network models. Numerical experiments demonstrate that systems that are robust with respect to perturbations in the parameter space have an easily “verifiable” (in terms of proof complexity) region of attraction properties. Motivated by the computational complexity of optimization-based formulations, we take a compositional approach and exploit a natural decomposition of the system, induced by the underlying biological structure, into a feedback interconnection of two input–output subsystems: a small subsystem with complicating nonlinearities and a large subsystem with simple dynamics. This decomposition simplifies the analysis of large pathways by assembling region of attraction certificates based on the input–output properties of the subsystems. It enables numerical as well as analytical construction of block-diagonal Lyapunov functions for a large family of autocatalytic pathways.     

A global piecewise smooth Newton method for fast large-scale model predictive control

In this paper, the strictly convex quadratic program (QP) arising in model predictive control (MPC) for constrained linear systems is reformulated as a system of piecewise affine equations. A regularized piecewise smooth Newton method with exact line search on a convex, differentiable, piecewise-quadratic merit function is proposed for the solution of the reformulated problem. The algorithm has considerable merits when applied to MPC over standard active set or interior point algorithms. Its performance is tested and compared against state-of-the-art QP solvers on a series of benchmark problems. The proposed algorithm is orders of magnitudes faster, especially for large-scale problems and long horizons. For example, for the challenging crude distillation unit model of Pannocchia, Rawlings, and Wright (2007) with 252 states, 32 inputs, and 90 outputs, the average running time of the proposed approach is 1.57 ms.     

On singular perturbations due to fast actuators in hybrid control systems

A stability result is given for hybrid control systems singularly perturbed by fast but continuous actuators. If a hybrid control system has a compact set globally asymptotically stable when the actuator dynamics are omitted, or equivalently, are infinitely fast, then the same compact set is semiglobally practically asymptotically stable in the finite speed of the actuator dynamics. This result, which generalizes classical results for differential equations, justifies using a simplified plant model that ignores fast but continuous actuator dynamics, even when using a hybrid feedback control algorithm.     

Active learning paradigms for CBIR systems based on optimum-path forest classification

This paper discusses methods for content-based image retrieval (CBIR) systems based on relevance feedback according to two active learning paradigms, named greedy and planned. In greedy methods, the system aims to return the most relevant images for a query at each iteration. In planned methods, the most informative images are returned during a few iterations and the most relevant ones are only presented afterward. In the past, we proposed a greedy approach based on optimum-path forest classification (OPF) and demonstrated its gain in effectiveness with respect to a planned method based on support-vector machines and another greedy approach based on multi-point query. In this work, we introduce a planned approach based on the OPF classifier and demonstrate its gain in effectiveness over all methods above using more image databases. In our tests, the most informative images are better obtained from images that are classified as relevant, which differs from the original definition. The results also indicate that both OPF-based methods require less user involvement (efficiency) to satisfy the user's expectation (effectiveness), and provide interactive response times.     

Gender discriminating models from facial surface normals

In this paper, we show how to use facial shape information to construct discriminating models for gender classification. We represent facial shapes using 2.5D fields of facial surface normals, and investigate three different methods to improve the gender discriminating capacity of the model constructed using the standard eigenspace method. The three methods are novel variants of principal geodesic analysis (PGA) namely (a) weighted PGA, (b) supervised weighted PGA, and (c) supervised PGA. Our starting point is to define a weight map over the facial surface that indicates the importance of different locations in discriminating gender. We show how to compute the relevant weights and how to incorporate the weights into the 2.5D model construction. We evaluate the performance of the alternative methods using facial surface normals extracted from 3D range images or recovered from brightness images. Experimental results demonstrate the effectiveness of our methods. Moreover, the classification accuracy, which is as high as 97%, demonstrates the effectiveness of using facial shape information for gender classification.     

Fuzzy identity based signature with applications to biometric authentication

We introduce a new cryptographic primitive which is the signature analog of fuzzy identity based encryption (FIBE). We call it fuzzy identity based signature (FIBS). It possesses similar error-tolerance property as FIBE that allows a user with the private key for identity ω to decrypt a ciphertext encrypted for identity ω′ if and only if ω and ω′ are within a certain distance judged by some metric. We give the definition and security model of FIBS and present the first practical FIBS scheme. We prove that our scheme is existentially unforgeable against adaptively chosen message attack in the standard model. To our best knowledge, this primitive was never considered in the identity based signature before. FIBS is of particular value for biometric authentication, where biometric identifiers such as fingerprints, iris, voice and gait are used in human identification. We demonstrate the applicability of our construction to secure biometric authentication.     

Classifying the Geometric Dilution of Precision of GPS satellites utilizing Bayesian decision theory

The errors resulting from satellite configuration geometry can be determined by Geometric Dilution of Precision (GDOP). Considering optimal satellite subset selection, lower GDOP value usually causes better accuracy in GPS positioning. However, GDOP computation based on complicated transformation and inversion of measurement matrices is a time consuming procedure. This paper deals with classification of GPS GDOP utilizing Parzen estimation based Bayesian decision theory. The conditional probability of each class is estimated by Parzen algorithm. Then based on Bayesian decision theory, the class with maximum posterior probability is selected. The experiments on measured dataset demonstrate that the proposed algorithm lead, in mean classification improvement, to 4.08% in comparison with Support Vector Machine (SVM) and 9.83% in comparison with K-Nearest Neighbour (KNN) classifier. Extra work on feature extraction has been performed based on Principle Component Analysis (PCA). The results demonstrate that the feature extraction approach has best performance respect to all classifiers.     

A robust and efficient algorithm for distributed compressed sensing

In this paper we present a new iterative greedy algorithm for distributed compressed sensing (DCS) problem based on the backtracking technique, which can reconstruct several input signals simultaneously by processing column by column of the compressed signals, even when the measurements are contaminated with noise and without any prior information of their sparseness. This makes it a promising candidate for many practical applications when the number of non-zero (significant) coefficients of a signal is not available. Our algorithm can provide a fast runtime while also offers comparably theoretical guarantees as the best optimization-based approach in both the noiseless and noisy regime. Numerical experiments are performed to demonstrate the validity and high performance of the proposed algorithm.     

Wire antennas optimized using genetic algorithm

In this work, wire antennas are designed to jam GSM frequencies using genetic algorithms. These antennas are designed to block communication at 3-band GSM frequencies. They are planned to be mounted on a vehicle and therefore are modeled on a ground plane. Jammer antennas designed in this work are composed of wires, placed on two square dielectric frames, perpendicular to each other. Genetic optimization routines are developed on MATLAB environment for the designs carried out in this work. Electromagnetic simulation program called SuperNEC, which analyses antennas by the method of moments, is used to determine the antenna performances and is called by the developed GA routines. The purpose of the antenna optimization is to obtain low VSWR values and omni-directional radiation pattern near ground at θ = [70° 80° 90°] planes at all GSM frequencies, since the targets will be near ground for this application. Original and interesting antenna designs are obtained as the result of genetic optimization, and are presented in the paper.