Three-dimensional facial surface modeling applied to recognition

Applications related to game technology, law-enforcement, security, medicine or biometrics are becoming increasingly important, which, combined with the proliferation of three-dimensional (3D) scanning hardware, have made that 3D face recognition is now becoming a promising and feasible alternative to two-dimensional (2D) face methods. The main advantage of 3D data, when compared with traditional 2D approaches, is that it provides information that is invariant to rigid geometric transformations and to pose and illumination conditions. One key element for any 3D face recognition system is the modeling of the available scanned data. This paper presents new 3D models for facial surface representation and evaluates them using two matching approaches: one based on support vector machines and another one on principal component analysis (with a Euclidean classifier). Also, two types of environments were tested in order to check the robustness of the proposed models: a controlled environment with respect to facial conditions (i.e. expressions, face rotations, etc.) and a non-controlled one (presenting face rotations and pronounced facial expressions). The recognition rates obtained using reduced spatial resolution representations (a 77.86% for non-controlled environments and a 90.16% for controlled environments, respectively) show that the proposed models can be effectively used for practical face recognition applications.     

Discontinuous gradient algorithm for finite-time estimation of time-varying parameters

ABSTRACT

In this work, we present a discontinuous algorithm capable of estimating time-varying parameters in finite time. The measured output is assumed to be linear in the parameters, i.e. it corresponds to a linear parametric model. It is further assumed that the parameter variation is uniformly bounded, and that the regressor is sufficiently exciting as to make the parameters identifiable.     

Robotics in Education

Collision avoidance is essential for safe robot manipulation. Especially with humans around, robots should work only when safety can be robustly guaranteed. In this paper, we propose using virtual impedance control for reactive, smooth, and consistent collision avoidance that interferes minimally with the original task. The virtual impedance control operates in the risk space, a vector space describing the possibilities of all forthcoming collisions, and is designed to elude all risks in a consistent response in order to create assuring human-robot interaction experiences. The proposed scheme intrinsically handles kinematic singularity and the activation of avoidance using a boundary layer defined on the spectrum of Jacobian. In cooperation with the original controller, the proposed avoidance scheme provides a proof of convergence if the original controller is stable with and without projection. In simulations and experiments, we verified the characteristics of the proposed control scheme and integrated the system with Microsoft Kinect to monitor the workspace for real-time collision detection and avoidance. The results show that the proposed approach is suitable for robot operation with humans nearby.     

Catalytic oxidation with Al-Ce-Fe-PILC as a post-treatment system for coffee wet processing wastewater

The effluent from the anaerobic biological treatment of coffee wet processing wastewater (CWPW) contains a non-biodegradable compound that must be treated before it is discharged into a water source. In this paper, the wet hydrogen peroxide catalytic oxidation (WHPCO) process using Al-Ce-Fe-PILC catalysts was researched as a post-treatment system for CWPW and tested in a semi-batch reactor at atmospheric pressure and 25 °C. The Al-Ce-Fe-PILC achieved a high conversion rate of total phenolic compounds (70%) and mineralization to CO(2) (50%) after 5 h reaction time. The chemical oxygen demand (COD) of coffee processing wastewater after wet hydrogen peroxide catalytic oxidation was reduced in 66%. The combination of the two treatment methods, biological (developed by Cenicafé) and catalytic oxidation with Al-Ce-Fe-PILC, achieved a 97% reduction of COD in CWPW. Therefore, the WHPCO using Al-Ce-Fe-PILC catalysts is a viable alternative for the post-treatment of coffee processing wastewater.     

A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics

Considering that group formation is one of the key processes in collaborative learning, the aim of this paper is to propose a method based on a genetic algorithm approach for achieving inter-homogeneous and intra-heterogeneous groups. The main feature of such a method is that it allows for the consideration of as many student characteristics as may be desired, translating the grouping problem into one of multi-objective optimization. In order to validate our approach, an experiment was designed with 135 college freshmen considering three characteristics: an estimate of student knowledge levels, an estimate of student communicative skills, and an estimate of student leadership skills. Results of such an experiment allowed for the validation, not only from the computational point of view by measuring the algorithmic performance, but also from the pedagogical point of view by measuring student outcomes, and comparing them with two traditional group formation strategies: random and self-organized.     

Intrinsic Images by Clustering

Decomposing an input image into its intrinsic shading and reflectance components is a long‐standing ill‐posed problem. We present a novel algorithm that requires no user strokes and works on a single image. Based on simple assumptions about its reflectance and luminance, we first find clusters of similar reflectance in the image, and build a linear system describing the connections and relations between them. Our assumptions are less restrictive than widely‐adopted Retinex‐based approaches, and can be further relaxed in conflicting situations. The resulting system is robust even in the presence of areas where our assumptions do not hold. We show a wide variety of results, including natural images, objects from the MIT dataset and texture images, along with several applications, proving the versatility of our method.     

A Narrowing-based Instantiation Rule for Rewriting-based Fold/Unfold Transformations

In this paper we show how to transfer some developments done in the field of functionallogic programming (FLP) to a pure functional setting (FP). More exactly, we propose a complete fold/unfold based transformation system for optimizing lazy functional programs. Our main contribution is the definition of a safe instantiation rule which is used to enable effective unfolding steps based on rewriting. Since instantiation has been traditionally considered problematic in FP, we take advantage of previous experiences in the more general setting of FLP where instantiation is naturally embedded into an unfolding rule based on narrowing. Inspired by the so called needed narrowing strategy, our instantiation rule inherits the best properties of this refinement of narrowing. Our proposal optimizes previous approaches (that require more transformation effort) defined in the specialized literature of pure FP by anticipating bindings on unifiers used to instantiate a given program rule and by generating redexes at different positions on instantiated rules in order to enable subsequent unfolding steps. As a consequence, our correct/complete technique avoids redundant rules and preserves the natural structure of programs.     

Analysis of inorganic anions in drainage water and soil solution by single-column ion chromatography

Single-column ion chromatography (SCIC) for anion determination in drainage water and soil solution was tested. The SCIC minimum detection limits (100-microliter sample loop) were 0.75 mg l-1 for Cl-, 0.2 mg l-1 for NO2-N, 0.02 mg l-1 for NO3-N, 1.25 mg l-1 for HPO4-P, and 0.5 mg l-1 for SO4-S. The results showed a high reproducibility. Results for Cl-, NO3-N and SO4-S obtained by the SCIC method were compared with those obtained by traditional methods; Student's t-test and regression analysis showed that the methods agree closely.     

Modafinil Administration to Preadolescent Rat Impairs Non-Selective Attention,Frontal Cortex D2 Expression and Mesolimbic GABA Levels

The misuse of psychostimulants is an increasing behavior among young people, highlighting in some countries the abuse of modafinil (MOD) as a neuropotentiator. However, several clinical trials are investigating MOD as an alternative pharmacological treatment for attentional deficit and hyperactivity disorder (ADHD) in children and adolescents. On the other hand, the early use of psychostimulants and the misdiagnosis rates in ADHD make it crucial to investigate the brain effects of this type of drug in young healthy individuals. The aim of this work was to evaluate the effects of chronic MOD treatment on neurochemicals (γ-aminobutyric acid and glutamate), dopamine receptor 2 (D₂) expression and behavior (non-selective attention “NSA”) in the mesocorticolimbic system of young healthy Sprague–Dawley rats. Preadolescent male rats were injected with MOD (75 mg/kg, i.p.) or a vehicle for 14 days (from postnatal day 22 to 35). At postnatal day 36, we measured the GLU and GABA contents and their extracellular levels in the nucleus accumbens (NAc). In addition, the GLU and GABA contents were measured in the ventral tegmental area (VTA) and D₂ protein levels in the prefrontal cortex (PFC). Chronic use of MOD during adolescence induces behavioral and neurochemical changes associated with the mesocorticolimbic system, such as a reduction in PFC D₂ expression, VTA GABA levels and NSA. These results contribute to the understanding of the neurological effects of chronic MOD use on a young healthy brain.