A novel humid electronic nose combined with an electronic tongue for assessing deterioration of wine

We report herein the use of a combined system for the analysis of the spoilage of wine when in contact with air. The system consists of a potentiometric electronic tongue and a humid electronic nose. The potentiometric electronic tongue was built with thick-film serigraphic techniques using commercially available resistances and conductors for hybrid electronic circuits; i.e. Ag, Au, Cu, Ru, AgCl, and C. The humid electronic nose was designed in order to detect vapours that emanate from the wine and are apprehended by a moist environment. The humid nose was constructed using a piece of thin cloth sewn, damped with distilled water, forming five hollows of the right size to introduce the electrodes. In this particular case four electrodes were used for the humid electronic nose: a glass electrode, aluminium (Al), graphite and platinum (Pt) wires and an Ag-AgCl reference electrode. The humid electronic nose together with the potentiometric electronic tongue were used for the evaluation of the evolution in the course of time of wine samples. Additionally to the analysis performed by the tongue and nose, the spoilage of the wines was followed via a simple determination of the titratable (total) acidity.     

Pull-in-based μg-resolution accelerometer: Characterization and noise analysis

The pull-in time (t_pi) of electrostatically actuated parallel-plate microstructures enables the realization of a high-sensitivity accelerometer that uses time measurement as the transduction mechanism. The key feature is the existence of a metastable region that dominates pull-in behavior, thus making pull-in time very sensitive to external accelerations. Parallel-plate MEMS structures have been designed and fabricated using a SOI micromachining process (SOIMUMPS) for the implementation of the accelerometer. This paper presents the experimental characterization of the microdevices, validating the concept and the analytical models used. The accelerometer has a measured sensitivity of 0.25 μs/μg and a bandwidth that is directly related to the pull-in time, BW = 1/2t_pi ≈ 50 Hz. These specifications place this sensor between the state of the art accelerometers found both in the literature and commercially. More importantly, the resolution of the measurement method used is very high, making the mechanical-thermal noise the only factor limiting the resolution. The in-depth noise analysis to the system supports these conclusions. The total measured noise floor of 400 μg (100 μs) is mainly due to the contribution of the environmental noise, due to lack of isolation of the experimental setup from the building vibrations (estimated mechanical thermal noise of 2.8 μg/√Hz). The low requirements of the electronic readout circuit makes this an interesting approach for high-resolution accelerometers.     

Designing adaptive learning itineraries using features modelling and swarm intelligence

In this paper, Bayesian network (BN) and ant colony optimization (ACO) techniques are combined in order to find the best path through a graph representing all available itineraries to acquire a professional competence. The combination of these methods allows us to design a dynamic learning path, useful in a rapidly changing world. One of the most important advances in this work, apart from the variable amount of pheromones, is the automatic processing of the learning graph. This processing is carried out by the learning management system and helps towards understanding the learning process as a competence-oriented itinerary instead of a stand-alone course. The amount of pheromones is calculated by taking into account the results acquired in the last completed course in relation to the minimum score required and by feeding this into the learning tree in order to obtain a relative impact on the path taken by the student. A BN is used to predict the probability of success, by taking historical data and student profiles into account. Usually, these profiles are defined beforehand; however, in our approach, some characteristics of these profiles, such as the level of knowledge, are classified automatically through supervised and/or unsupervised learning. By using ACO and BN, a fitness function, responsible for automatically selecting the next course in the learning graph, is defined. This is done by generating a path which maximizes the probability of each user??s success on the course. Therefore, the path can change in order to adapt itself to learners?? preferences and needs, by taking into account the pedagogical weight of each learning unit and the social behaviour of the system.     

Extracting significant Website Key Objects: A Semantic Web mining approach

Web mining has been traditionally used in different application domains in order to enhance the content that Web users are accessing. Likewise, Website administrators are interested in finding new approaches to improve their Website content according to their users' preferences. Furthermore, the Semantic Web has been considered as an alternative to represent Web content in a way which can be used by intelligent techniques to provide the organization, meaning, and definition of Web content. In this work, we define the Website Key Object Extraction problem, whose solution is based on a Semantic Web mining approach to extract from a given Website core ontology, new relations between objects according to their Web user interests. This methodology was applied to a real Website, whose results showed that the automatic extraction of Key Objects is highly competitive against traditional surveys applied to Web users.     

Moving objects forecast in image sequences using autoregressive algorithms

The objective of this paper is to present an overall approach to forecasting the future position of the moving objects of an image sequence after processing the images previous to it. The proposed method makes use of classical techniques such as optical flow to extract objects’ trajectories and velocities, and autoregressive algorithms to build the predictive model. Our method can be used in a variety of applications, where videos with stationary cameras are used, moving objects are not deformed and change their position with time. One of these applications is traffic control, which is used in this paper as a case study with different meteorological conditions to compare with.

Atom-Aid: Detecting and Surviving Atomicity Violations

Hardware can play a significant role in improving reliability of multithreaded software. Recent architectural proposals arbitrarily group consecutive dynamic memory operations into atomic blocks to enforce coarse-grained memory ordering, providing implicit atomicity. The authors of this article observe that implicit atomicity probabilistically hides atomicity violations by reducing the number of interleaving opportunities between memory operations. They propose Atom-Aid, which creates implicit atomic blocks intelligently instead of arbitrarily, dramatically reducing the probability that atomicity violations will manifest themselves.     

Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms

We describe a tracking controller for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on type-2 fuzzy logic theory and genetic algorithms. Computer simulations are presented confirming the performance of the tracking controller and its application to different navigation problems.     

Aerodynamic and aeroelastic analyses on the CAARC standard tall building model using numerical simulation

The simulation of the wind action over the CAARC (Commonwealth Advisory Aeronautical Council) standard tall building model is performed in the present work. Aerodynamic and aeroelastic analyses are reproduced numerically in order to demonstrate the applicability of CFD techniques in the field of wind engineering. A major topic in this paper is referred to one of the first attempts to simulate the aeroelastic behavior of a tall building employing complex CFD techniques. Numerical results obtained in this work are compared with numerical and wind tunnel measurements and some important concluding remarks about the present simulation are also reported.     

MOE prediction in Abies pinsapo Boiss. timber: Application of an artificial neural network using non-destructive testing

Determining the modulus of elasticity of wood by applying an artificial neural network using the physical properties and non-destructive testing can be a useful method in assessments of the timber structure in old constructions. The modulus of elasticity of Abies pinsapo Boiss. timber was predicted in this study through the parameters of density, width, thickness, moisture content, ultrasonic wave propagation velocity and visual grading of the test pieces. A feedforward multilayer perceptron network was designed for this purpose, achieving 75.0% success in the testing or unknown group.     

Unmanned Aerial Vehicle Localization Based on Monocular Vision and Online Mosaicking

This paper presents a new approach for vision-based UAV localization, using mosaics as environment representations. Inter-image motions are used to estimate the motion of the UAV. Online mosaicking is applied to reduce the impact of the accumulative errors in UAV position estimation. A new method to build an stochastic mosaic given the image-to-image homographies is detailed. The mosaic consists of a network of inter-image relations, and is used to create a consistent view of the environment of the UAV and hence, to detect the drift in position estimation by using the mosaic as a resource. The technique could be called simultaneous localization and mosaicking. This technique is specially suitable for monitoring and surveillance tasks in which the UAV will repeatedly cover the same area. The paper also shows experimental results with real UAVs where the benefits of the proposed method are evident.