Face recognition with lattice independent component analysis and extreme learning machines

We focus on two aspects of the face recognition, feature extraction and classification. We propose a two component system, introducing Lattice Independent Component Analysis (LICA) for feature extraction and Extreme Learning Machines (ELM) for classification. In previous works we have proposed LICA for a variety of image processing tasks. The first step of LICA is to identify strong lattice independent components from the data. In the second step, the set of strong lattice independent vector are used for linear unmixing of the data, obtaining a vector of abundance coefficients. The resulting abundance values are used as features for classification, specifically for face recognition. Extreme Learning Machines are accurate and fast-learning innovative classification methods based on the random generation of the input-to-hidden-units weights followed by the resolution of the linear equations to obtain the hidden-to-output weights. The LICA-ELM system has been tested against state-of-the-art feature extraction methods and classifiers, outperforming them when performing cross-validation on four large unbalanced face databases.     

Generalized extreme learning machine acting on a metric space

Functional data learning is an extension of traditional data learning, that is, learning the data chosen from the Euclidean space ${\mathbb{R}^{n}}$ to a metric space. This paper focuses on the functional data learning with generalized single-hidden layer feedforward neural networks (GSLFNs) acting on some metric spaces. In addition, three learning algorithms, named Hilbert parallel overrelaxation backpropagation (H-PORBP) algorithm, ν-generalized support vector regression (ν-GSVR) and generalized extreme learning machine (G-ELM) are proposed to train the GSLFNs acting on some metric spaces. The experimental results on some metric spaces indicate that GELM with additive/RBF hidden-nodes has a faster learning speed, a better accuracy, and a better stability than HPORBP algorithm and ν-GSVR for training the functional data. The idea of GELM can be used to extend those improved extreme learning machines (ELMs) that act on the Euclidean space ${\mathbb{R}^{n}, }$ such as online sequential ELM, incremental ELM, pruning ELM and so on, to some metric spaces.     

Extension of belief functions to infinite-valued events

We generalise belief functions to many-valued events which are represented by elements of Lindenbaum algebra of infinite-valued ?ukasiewicz propositional logic. Our approach is based on mass assignments used in the Dempster–Shafer theory of evidence. A generalised belief function is totally monotone and it has Choquet integral representation with respect to a unique belief measure on Boolean events.     

Faster Approximate String Matching for Short Patterns

We study the classical approximate string matching problem, that is, given strings P and Q and an error threshold k, find all ending positions of substrings of Q whose edit distance to P is at most k. Let P and Q have lengths m and n, respectively. On a standard unit-cost word RAM with word size w≥log n we present an algorithm using time

Performance Evaluation of Range Queries in Key Value Stores

Recently there has been a considerable increase in the number of different Key-Value stores, for supporting data storage and applications on the cloud environment. While all these solutions try to offer highly available and scalable services on the cloud, they are significantly different with each other in terms of the architecture and types of the applications, they try to support. Considering three widely-used such systems: Cassandra, HBase and Voldemort; in this paper we compare them in terms of their support for different types of query workloads. We are mainly focused on the range queries. Unlike HBase and Cassandra that have built-in support for range queries, Voldemort does not support this type of queries via its available API. For this matter, practical techniques are presented on top of Voldemort to support range queries. Our performance evaluation is based on mixed query workloads, in the sense that they contain a combination of short and long range queries, beside other types of typical queries on key-value stores such as lookup and update. We show that there are trade-offs in the performance of the selected system and scheme, and the types of the query workloads that can be processed efficiently.     

Entanglement mean field theory: Lipkin–Meshkov–Glick Model

Entanglement mean field theory is an approximate method for dealing with many-body systems, especially for the prediction of the onset of phase transitions. While previous studies have concentrated mainly on applications of the theory on short-range interaction models, we show here that it can be efficiently applied also to systems with long-range interaction Hamiltonians. We consider the (quantum) Lipkin–Meshkov–Glick spin model, and derive the entanglement mean field theory reduced Hamiltonian. A similar recipe can be applied to obtain entanglement mean field theory reduced Hamiltonians corresponding to other long-range interaction systems. We show, in particular, that the zero temperature quantum phase transition present in the Lipkin–Meshkov–Glick model can be accurately predicted by the theory.     

Signal tracking method of GNSS receivers for spinning vehicles

A signal tracking method of GNSS receivers for spinning vehicles is proposed in order to eliminate the influence of spinning to the GNSS signal of a vehicle. In the proposed method, a rotation tracking loop is added into the carrier tracking loop of a conventional GNSS receiver. The proposed method was verified through computer simulations and an experiment for live GPS signals. The results show that the proposed method gives an accurate tracking performance.     

Millisecond-order rapid micromixing with non-equilibrium electrokinetic phenomena

We report an active micromixer utilizing vortex generation due to non-equilibrium electrokinetics near micro/nanochannel interfaces. Its design is relatively simple, consisting of a U-shaped microchannel and a set of nanochannels. We fabricated the micromixer just using a two-step reactive ion etching process. We observed strong vortex generation in fluorescent microscopy experiments. The mixing performance was evident in a combined pressure-driven and electroosmotic flows, compared with the case with a pure pressure-driven flow. We characterized the micromixer for several conditions: different applied voltages, ion concentrations, flow rates, and nanochannel widths. The experimental results show that the mixing performance is better with a higher applied voltage, a lower ion concentration, and a wider nanochannel width. We quantified the mixing characteristics in terms of mixing time. The lowest mixing time was 2 milliseconds with the voltage of 230 V and potassium chloride solutions of 0.1 mM. We expect that the micromixer is beneficial in several applications requiring rapid mixing.     

Superparamagnetic particle dynamics and mixing in a rotating capillary tube with a stationary magnetic field

The dynamics of superparamagnetic particles subject to competing magnetic and viscous drag forces have been examined with a uniform, stationary, external magnetic field. In this approach, competing drag and magnetic forces were created in a fluid suspension of superparamagnetic particles that was confined in a capillary tube; competing viscous drag and magnetic forces were established by rotating the tube. A critical Mason number was determined for conditions under which the rotation of the capillary prevents the formation of chains from individual particles. The statistics of chain length was investigated by image analysis while varying parameters such as the rotation speed and the viscosity of the liquid. The measurements showed that the rate of particle chain formation was decreased with increased viscosity and rotation speed; the particle dynamics could be quantified by the same dimensionless Mason number that has been demonstrated for rotating magnetic fields. The potential for enhancement of mixing in a bioassay was assessed using a fast chemical reaction that was diffusion-limited. Reducing the Mason number below the critical value, so that chains were formed in the fluid, gave rise to a modest improvement in the time to completion of the reaction.     

Optimum topology design of various geometrically nonlinear latticed domes using improved harmony search method

Domes are elegant and economical structures used in covering large areas. They are built in various forms. According to their form, they are given special names such as lamella, network, and geodesic domes. In this paper, optimum topological design algorithm is presented that determines the optimum number of rings, the optimum height of crown and tubular section designations for the member groups of these domes. The design algorithm developed has a routine that generates the data required for the geometry of these domes automatically. The minimum weight of each dome is taken as the objective function. The design constraints are implemented according to the provision of LRFD-AISC (Load and Resistance Factor Design–American Institute of Steel Constitution). The optimum topological design problem that considers these constraints turns out to be discrete programming problem. Improved harmony search algorithm is suggested to determine its optimum solution. The design algorithm also considers the geometric nonlinearity of these dome structures. Design examples are presented to demonstrate the effectiveness and robustness of the design optimization algorithm developed.