Microwave-Assisted Fabrication of Superamphiphobic Surfaces on Aluminum Substrates

The field of superamphiphobic surface fabrication has evolved rapidly in the last decade; however, research on important issues such as sustainability and green chemistry procedures is still scarce. Herein, a simple method of microwave irradiation (MW) to minimize energy consumption during the preparation of superamphiphobic aluminum (Al) surfaces is reported. Al substrates are first etched in diluted HCl solutions to generate a microstructure and then irradiated in a commercial microwave unit for several time intervals, temperatures, and pressures. The surfaces are then coated with different compounds, and the wettability is tested with high and very-low surface tension liquids. Optical profilometry and scanning electron microscopy images show that the density of hierarchical micro-nanostructures increases with MW time, temperature, and pressure. At 170 °C and 7.9 bar, the surfaces present a high density of structures and re-entrant topographies. The obtained coatings display excellent repellence to liquids with surface tensions as low as 27.5 mN m⁻¹. X-ray photoelectron spectroscopy data show the importance of efficient surface functionalization for the production of superamphiphobicity in Al substrates. The results show that MW irradiation of Al substrates can be a green and efficient method for fabricating superamphiphobic surfaces.     

Science macro-indicators: some aspects of ost experience

We report OST experience on macro-indicators producing, especially on academic science and ISI sources. This task requires a combination of organizational choices for data handling and processing, and of bibliometric choices for a selection of indicators appropriate to the missions. Both aspects are briefly studied: the OST database, which also contains nonbibliometric datasets, is organized on the relational principle (RDBMS). Bibliometric indicators selected are classical ones, with a stress on overall coherence. In conclusion, standardization issue is briefly discussed. Standardization may not be desirable at the same extent for different targets (data, nomenclatures, indicators, procedures, etc.) and must not hinder further research. Natural process of communication and explication may also lead to fruitful convergences, without freezing supposed best ways.     

Iris recognition: Analysis of the error rates regarding the accuracy of the segmentation stage

Iris recognition has been widely used in several scenarios with very satisfactory results. As it is one of the earliest stages, the image segmentation is in the basis of the process and plays a crucial role in the success of the recognition task. In this paper we analyze the relationship between the accuracy of the iris segmentation process and the error rates of three typical iris recognition methods. We selected 5000 images of the UBIRIS, CASIA and ICE databases that the used segmentation algorithm can accurately segment and artificially simulated four types of segmentation inaccuracies. The obtained results allowed us to conclude about a strong relationship between translational segmentation inaccuracies – that lead to errors in phase – and the recognition error rates.     

Spoken emotion recognition through optimum-path forest classification using glottal features

A new method for the recognition of spoken emotions is presented based on features of the glottal airflow signal. Its effectiveness is tested on the new optimum path classifier (OPF) as well as on six other previously established classification methods that included the Gaussian mixture model (GMM), support vector machine (SVM), artificial neural networks – multi layer perceptron (ANN-MLP), k-nearest neighbor rule (k-NN), Bayesian classifier (BC) and the C4.5 decision tree. The speech database used in this work was collected in an anechoic environment with ten speakers (5 M and 5 F) each speaking ten sentences in four different emotions: Happy, Angry, Sad, and Neutral. The glottal waveform was extracted from fluent speech via inverse filtering. The investigated features included the glottal symmetry and MFCC vectors of various lengths both for the glottal and the corresponding speech signal. Experimental results indicate that best performance is obtained for the glottal-only features with SVM and OPF generally providing the highest recognition rates, while for GMM or the combination of glottal and speech features performance was relatively inferior. For this text dependent, multi speaker task the top performing classifiers achieved perfect recognition rates for the case of 6th order glottal MFCCs.     

Search intensity versus search diversity: a false trade off?

An implicit tenet of modern search heuristics is that there is a mutually exclusive balance between two desirable goals: search diversity (or distribution), i.e., search through a maximum number of distinct areas, and, search intensity, i.e., a maximum search exploitation within each specific area. We claim that the hypothesis that these goals are mutually exclusive is false in parallel systems. We argue that it is possible to devise methods that exhibit high search intensity and high search diversity during the whole algorithmic execution. It is considered how distance metrics, i.e., functions for measuring diversity (given by the minimum number of local search steps between two solutions) and coordination policies, i.e., mechanisms for directing and redirecting search processes based on the information acquired by the distance metrics, can be used together to integrate a framework for the development of advanced collective search methods that present such desiderata of search intensity and search diversity under simultaneous coexistence. The presented model also avoids the undesirable occurrence of a problem we refer to as the ‘ergometric bike phenomenon’. Finally, this work is one of the very few analysis accomplished on a level of meta-meta-heuristics, because all arguments are independent of specific problems handled (such as scheduling, planning, etc.), of specific solution methods (such as genetic algorithms, simulated annealing, tabu search, etc.) and of specific neighborhood or genetic operators (2-opt, crossover, etc.).     

Vehicle inductive signatures recognition using a Madaline neural network

In this paper, we report results obtained with a Madaline neural network trained to classify inductive signatures of two vehicles classes: trucks with one rear axle and trucks with double rear axle. In order to train the Madaline, the inductive signatures were pre-processed and both classes, named C2 and C3, were subdivided into four subclasses. Thus, the initial classification task was split into four smaller tasks (theoretically) easier to be performed. The heuristic adopted in the training attempts to minimize the effects of the input space non-linearity on the classifier performance by uncoupling the learning of the classes and, for this, we induce output Adalines to specialize in learning one of the classes. The percentages of correct classifications presented concern patterns which were not submitted to the neural network in the training process, and, therefore, they indicate the neural network generalization ability. The results are good and stimulate the maintenance of this research on the use of Madaline networks in vehicle classification tasks using not linearly separable inductive signatures.     

A new framework for sharp and efficient resolution of NCSP with manifolds of solutions

When numerical CSPs are used to solve systems of n equations with n variables, the preconditioned interval Newton operator plays two key roles: First it allows handling the n equations as a global constraint, hence achieving a powerful contraction. Second it can prove rigorously the existence of solutions. However, none of these advantages can be used for under-constrained systems of equations, which have manifolds of solutions. A new framework is proposed in this paper to extend the advantages of the preconditioned interval Newton to under-constrained systems of equations. This is achieved simply by allowing domains of the NCSP to be parallelepipeds, which generalize the boxes usually used as domains.     

Engineering an efficient two‐phase local search algorithm for the co‐rotating twin‐screw extruder configuration problem

The twin‐screw configuration problem arises during polymer extrusion and compounding. It consists in defining the location of a set of pre‐defined screw elements along the screw axis in order to optimize different, typically conflicting objectives. In this paper, we present a simple yet effective stochastic local search (SLS) algorithm for this problem. Our algorithm is based on efficient single‐objective iterative improvement algorithms, which have been developed by studying different neighborhood structures, neighborhood search strategies, and neighborhood restrictions. These algorithms are embedded into a variation of the two‐phase local search framework to tackle various bi‐objective versions of this problem. An experimental comparison with a previously proposed multi‐objective evolutionary algorithm shows that a main advantage of our SLS algorithm is that it converges faster to a high‐quality approximation to the Pareto front.     

A new test scheduling algorithm based on Networks-on-Chip as Test Access Mechanisms

Networks-on-Chip (NoCs) can be used for test data transportation during manufacturing tests. On one hand, NoC can avoid dedicated Test Access Mechanisms (TAMs), reducing long global wires, and potentially simplifying the layout. On the other hand, (a) it is not known how much wiring is saved by reusing NoCs as TAMs, (b) the impact of reuse-based approaches on test time is not clear, and (c) a computer aided test tool must be able to support different types of NoC designs. This paper presents a test environment where the designer can quickly evaluate wiring and test time for different test architectures. Moreover, this paper presents a new test scheduling algorithm for NoC TAMs which does not require any NoC timing detail and it can easily model NoCs of different topologies. The experimental results evaluate the proposed algorithm for NoC TAMs with an exiting algorithm for dedicated TAMs. The results demonstrate that, on average, 24% (up to 58%) of the total global wires can be eliminated if dedicated TAMs are not used. Considering the reduced amount of dedicated test resources with NoC TAM, the test time of NoC TAM is only, on average, 3.88% longer compared to dedicated TAMs.     

Insights from a simple expression for linear fisher information in a recurrently connected population of spiking neurons

A simple expression for a lower bound of Fisher information is derived for a network of recurrently connected spiking neurons that have been driven to a noise-perturbed steady state. We call this lower bound linear Fisher information, as it corresponds to the Fisher information that can be recovered by a locally optimal linear estimator. Unlike recent similar calculations, the approach used here includes the effects of nonlinear gain functions and correlated input noise and yields a surprisingly simple and intuitive expression that offers substantial insight into the sources of information degradation across successive layers of a neural network. Here, this expression is used to (1) compute the optimal (i.e., information-maximizing) firing rate of a neuron, (2) demonstrate why sharpening tuning curves by either thresholding or the action of recurrent connectivity is generally a bad idea, (3) show how a single cortical expansion is sufficient to instantiate a redundant population code that can propagate across multiple cortical layers with minimal information loss, and (4) show that optimal recurrent connectivity strongly depends on the covariance structure of the inputs to the network.