Phase locked loop-based clock synthesizer for reconfigurable analog-to-digital converters

Analog Integrated Circuits and Signal Processing - This paper presents the complete design of a phase locked loop-based clock synthesizer for reconfigurable analog-to-digital converters. The...     

Ensemble of Efficient Minimal Learning Machines for Classification and Regression

Minimal Learning Machine (MLM) is a recently proposed supervised learning algorithm with performance comparable to most state-of-the-art machine learning methods. In this work, we propose ensemble methods for classification and regression using MLMs. The goal of ensemble strategies is to produce more robust and accurate models when compared to a single classifier or regression model. Despite its successful application, MLM employs a computationally intensive optimization problem as part of its test procedure (out-of-sample data estimation). This becomes even more noticeable in the context of ensemble learning, where multiple models are used. Aiming to provide fast alternatives to the standard MLM, we also propose the Nearest Neighbor Minimal Learning Machine and the Cubic Equation Minimal Learning Machine to cope with classification and single-output regression problems, respectively. The experimental assessment conducted on real-world datasets reports that ensemble of fast MLMs perform comparably or superiorly to reference machine learning algorithms.     

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.     

Response of Corn Seedlings (Zea mays L.) to Different Concentrations of Nitrogen in Absence and Presence of Silicon

Silicon - Corn plants are highly demanding of nitrogen and the application of silicon has been studied because it minimizes stress from different natures, and for the better utilization of some...     

Plant oil bioconversion into increase biological activity through lipases derived from wastes

The aim of this study was to show that abundant and inexpensive plant oils can be biotransformed to increase biological activity (antioxidant, antimicrobial and cytotoxicity) through hydrolysis reaction catalysed by lipases. We tested homemade and commercial lipases through the biotransformation of nine different plant oils in forty different combinations. First, the chemical composition of the samples was investigated. Thereafter, biological tests were conducted to assess the antioxidant and antimicrobial activity of the sampled biotransformation products, as well as analyzed their influence on the viability of healthy and cancer cells. Summarising, sunflower, corn and olive oils modified by orange waste-obtained lipases presented the most promising results, reaching up to 90% of antioxidant activity increase and significant growth inhibition of bacteria colonies belonging to genera Escherichia, Listeria, Staphylococcus, Pseudomonas and Salmonella. In addition, those compounds affected human oral squamous carcinoma cells. The bioconversion of plant oils through lipases improves their biological properties and might be an option for biotechnological application.     

Modification of coconut fibers with polyaniline for manufacture of pressure‐sensitive devices

This article studies the modification of coconut fibers with polyaniline (PAni) with help of statistical experimental design techniques. The main factors studied here were the techniques used for particle dispersion (sonication vs. magnetic stirring) and the type of initiator (ammonium persulfate, APS, vs. ammonium cerium sulfate dihydrate, Ce(IV)). The obtained materials were characterized through low field nuclear magnetic resonance, small‐and wide‐angle X‐ray scattering, scanning electron microscopy, fourier transform infrared spectroscopy, and ultraviolet–visible spectroscopy. Additionally, the electrical resistivities and respective sensitivities to variations of the applied pressure were evaluated for all obtained samples, with help of standard volume resistivity measurements and electro‐mechanical tests. The materials prepared through magnetic stirring with APS presented the best electrical and electromechanical properties, although materials prepared through sonication with Ce(IV) also presented good electrical and electromechanical properties and could be prepared much faster. As a consequence, modification of coconut fibers with PAni, using sonication as the particle dispersion technique and Ce(IV) as initiator, constitutes a very promising procedure for manufacture of pressure‐sensitive devices. POLYM. ENG. SCI., 54:2887–2895, 2014. © 2014 Society of Plastics Engineers     

Building a bridge between performance management,radical innovation,and innovation networks: A systematic literature review

Research on performance management (PM) of networks and radical innovation has largely developed along two isolated paths. On the one hand, a growing body of knowledge addresses the use of performance management systems in the context of networks (notably for production and incremental innovation). On the other hand, radical innovation scholars have shown how innovation initiatives that are more uncertain demand the use of new approaches (e.g., new project management routines or different evaluative tools for portfolios). Some scholars even suggest that traditional PM may suppress radical innovation. Our study takes a different approach. It aims to bridge radical innovation and network performance management through a systematic review of the literature. Here, we adopt a hybrid approach that combines bibliometrics with content analysis. Whereas the literature often separates the issues of network performance management and radical innovation, our contribution expands the theory of network management and radical innovation by developing a conceptual framework based on these concepts, thus combining them. We also offer suggestions for further research on this approach. Our framework suggests that overall, the performance management of radical innovation networks presents different features (e.g., recursive rather than linear) and constructs (e.g., openness and unintended performance) from those of innovation network management.