3D face computational photography using PCA spaces

In this paper, we present a 3D face photography system based on a facial expression training dataset, composed of both facial range images (3D geometry) and facial texture (2D photography). The proposed system allows one to obtain a 3D geometry representation of a given face provided as a 2D photography, which undergoes a series of transformations through the texture and geometry spaces estimated. In the training phase of the system, the facial landmarks are obtained by an active shape model (ASM) extracted from the 2D gray-level photography. Principal components analysis (PCA) is then used to represent the face dataset, thus defining an orthonormal basis of texture and another of geometry. In the reconstruction phase, an input is given by a face image to which the ASM is matched. The extracted facial landmarks and the face image are fed to the PCA basis transform, and a 3D version of the 2D input image is built. Experimental tests using a new dataset of 70 facial expressions belonging to ten subjects as training set show rapid reconstructed 3D faces which maintain spatial coherence similar to the human perception, thus corroborating the efficiency and the applicability of the proposed system.     

A DSS for integrated distribution of empty and full containers

A DSS integrating empty and full containers transshipment operations is presented, addressing the typically unbalanced export/import containers trading problem. The problem is modeled as a network, where nodes represent customers, leasing companies, harbors and warehouses, while arcs represent transportation routes. The underlying mathematical model operates in stages, first prioritizing and adjusting full containers demands considering available empty containers supplies, and then statically optimizing costs. Transportation routes are registered and dynamically controlled, cyclically, for a given time horizon. The DSS is flexible, allowing several parameters to be configured. Experimental examples using randomly generated parameters were conducted to evaluate the effectiveness of the system.     

Constructive learning neural network applied to identification and control of a fuel-ethanol fermentation process

In the present work, a constructive learning algorithm was employed to design a near-optimal one-hidden layer neural network structure that best approximates the dynamic behavior of a bioprocess. The method determines not only a proper number of hidden neurons but also the particular shape of the activation function for each node. Here, the projection pursuit technique was applied in association with the optimization of the solvability condition, giving rise to a more efficient and accurate computational learning algorithm. As each activation function of a hidden neuron is defined according to the peculiarities of each approximation problem, better rates of convergence are achieved, guiding to parsimonious neural network architectures. The proposed constructive learning algorithm was successfully applied to identify a MIMO bioprocess, providing a multivariable model that was able to describe the complex process dynamics, even in long-range horizon predictions. The resulting identification model was considered as part of a model-based predictive control strategy, producing high-quality performance in closed-loop experiments.     

Hand gesture recognition from depth and infrared Kinect data for CAVE applications interaction

This paper presents a real-time framework that combines depth data and infrared laser speckle pattern (ILSP) images, captured from a Kinect device, for static hand gesture recognition to interact with CAVE applications. At the startup of the system, background removal and hand position detection are performed using only the depth map. After that, tracking is started using the hand positions of the previous frames in order to seek for the hand centroid of the current one. The obtained point is used as a seed for a region growing algorithm to perform hand segmentation in the depth map. The result is a mask that will be used for hand segmentation in the ILSP frame sequence. Next, we apply motion restrictions for gesture spotting in order to mark each image as a ‘Gesture’ or ‘Non-Gesture’. The ILSP counterparts of the frames labeled as “Gesture” are enhanced by using mask subtraction, contrast stretching, median filter, and histogram equalization. The result is used as the input for the feature extraction using a scale invariant feature transform algorithm (SIFT), bag-of-visual-words construction and classification through a multi-class support vector machine (SVM) classifier. Finally, we build a grammar based on the hand gesture classes to convert the classification results in control commands for the CAVE application. The performed tests and comparisons show that the implemented plugin is an efficient solution. We achieve state-of-the-art recognition accuracy as well as efficient object manipulation in a virtual scene visualized in the CAVE.     

An adaptive learning approach for 3-D surface reconstruction from point clouds.

In this paper, we propose a multiresolution approach for surface reconstruction from clouds of unorganized points representing an object surface in 3-D space. The proposed method uses a set of mesh operators and simple rules for selective mesh refinement, with a strategy based on Kohonen's self-organizing map (SOM). Basically, a self-adaptive scheme is used for iteratively moving vertices of an initial simple mesh in the direction of the set of points, ideally the object boundary. Successive refinement and motion of vertices are applied leading to a more detailed surface, in a multiresolution, iterative scheme. Reconstruction was experimented on with several point sets, including different shapes and sizes. Results show generated meshes very close to object final shapes. We include measures of performance and discuss robustness.     

A Framework for Integrating Non-Functional Requirements into Conceptual Models

The development of complex information systems calls for conceptual models that describe aspects beyond entities and activities. In particular, recent research has pointed out that conceptual models need to model goals, in order to capture the intentions which underlie complex situations within an organisational context. This paper focuses on one class of goals, namely non-functional requirements (NFR), which need to be captured and analysed from the very early phases of the software development process. The paper presents a framework for integrating NFRs into the ER and OO models. This framework has been validated by two case studies, one of which is very large. The results of the case studies suggest that goal modelling during early phases can lead to a more productive and complete modelling activity.       

Preparation and characterization of new niobophosphate glasses in the Li2O-Nb2O5-CaO-P2O5 system

In the present work we describe the synthesis, spectroscopy, thermal and chemical durability properties of the vitreous system Li₂O-Nb₂O₅-CaO-P₂O₅ (LNCP). Investigations of the short-range order by Fourier transform infrared, Raman, UV-VIS and ³¹P MAS-NMR spectroscopies suggest that the network former glass consists of Nb octahedra linked to pyro/orthophosphate units through Nb—O—P bonds. The presence of modifier cations (Li⁺ and Ca²⁺) promotes depolymerization of the P—O—P chains, yielding pyro/orthophosphate units. The presence of this kind of structure accounts for the improvement of the chemical durability at low pH when the Nb content in the LNCP glass composition is high. The density and linear refractive indices of LNCP glasses increased linearly as the Nb₂O₅/P₂O₅ molar ratio increased, as a consequence of P₂O₅ substitution by Nb₂O₅ as the glassformer. The dependence of the glass transition temperature, the softening temperature and the crystallization temperature on the Nb₂O₅/P₂O₅ ratio exhibits the same behavior. On the other hand, the thermal expansion coefficient decreases with the increased Nb₂O₅/P₂O₅ ratio.     

New approach to immobilization and specific-sequence detection of nucleic acids based on poly(4-hydroxyphenylacetic acid)

Immobilization and hybridization of oligonucleotides or specific-gene PCR product (DENV-1), a conserved genomic sequence of the dengue virus, onto graphite electrode modified with poly(4-hydroxyphenylacetic acid), were carried out with success using both direct electrochemical oxidation of guanine or redox electroactive indicator ethidium bromide.Studies of oligonucleotides hybridization with the complementary target showed a decrease of both guanosine and adenosine current peaks, when compared with the peak previously obtained before the hybridization. Immobilized ssDNA, DENV-1, was hybridized with various concentrations of target DNA. The interaction between DENV-1 hybridized onto the modified graphite electrodes surface and the intercalator, ethidium bromide, was observed by differential pulse voltammetry, monitoring the current change generated to the DNA intercalator accumulated onto the modified electrode after DNA hybridization. For the determination of complementary target, the proposed method exhibited a good dynamic range (12–42 nmol L^? 1) and a low detection limit (7.12 nmol L^? 1).AFM images showed that the oligonucleotides or single-stranded DNA, DENV-1, before hybridization, had roughness values lower than the double stranded obtained after hybridization.The new surface obtained in these work, as well as the possibility of utilization of the same to monitor hybridization events is a promising strategy for the development of DNA electrochemical biosensors.