Determinants of internal and external R&D offshoring: evidence from Spanish firms

This paper analyses the determinants of R&D offshoring of Spanish firms using information from the Panel of Technological Innovation. We find that being an exporter, international technological cooperation, continuous R&D engagement, applying for patents, being a foreign subsidiary, and firm size are factors that positively affect the decision to offshore R&D. In addition, we find that a lack of financing is an obstacle relatively more important for independent firms than for firms that belong to business groups. For these latter, we also obtain that the factors that influence the decision to offshore R&D differ depending on whether the firm purchases the R&D services within the group or through the market: a higher degree of importance assigned to internal sources of information for innovation as compared to market sources increases (decreases) the probability of R&D offshoring only through the group (market).     

Object Recognition in X-ray Testing Using Adaptive Sparse Representations

In recent years, X-ray screening systems have been used to safeguard environments in which access control is of paramount importance. Security checkpoints have been placed at the entrances to many public places to detect prohibited items such as handguns and explosives. Human operators complete these tasks because automated recognition in baggage inspection is far from perfect. Research and development on X-ray testing is, however, ongoing into new approaches that can be used to aid human operators. This paper attempts to make a contribution to the field of object recognition by proposing a new approach called Adaptive Sparse Representation (XASR+). It consists of two stages: learning and testing. In the learning stage, for each object of training dataset, several patches are extracted from its X-ray images in order to construct representative dictionaries. A stop-list is used to remove very common words of the dictionaries. In the testing stage, test patches of the test image are extracted, and for each test patch a dictionary is built concatenating the ‘best’ representative dictionary of each object. Using this adapted dictionary, each test patch is classified following the Sparse Representation Classification methodology. Finally, the test image is classified by patch voting. Thus, our approach is able to deal with less constrained conditions including some contrast variability, pose, intra-class variability, size of the image and focal distance. We tested the effectiveness of our method for the detection of four different objects. In our experiments, the recognition rate was more than 97 % in each class, and more than 94 % if the object is occluded less than 15 %. Results show that XASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature.     

A survey of land mine detection technology

This paper describes the state of the art in land mine detection technology and algorithms. Landmine detection is a growing concern due to the danger of buried landmines to people's lives, economic growth and development. Most of the injured people have no connection with the reason why the mines were placed. There are 50–100 million landmines in more than 80 countries around the world. Deactivation is estimated at 100 000 mines per year, against the nearly 2 million mines laid annually. In this paper we describe and analyse sensor technology available including state‐of‐the‐art technology such as ground penetrating radar (GPR), electromagnetic induction (EMI) and nuclear quadrupole resonance (NQR) among others. Robotics, data processing and algorithms are mentioned, considering support vectors, sensor fusion, neural networks, etc. Finally, we establish conclusions highlighting the need to improve not only the way images are acquired, but the way this information is processed and compared.     

Determination of metals in wine with atomic spectroscopy (flame-AAS, GF-AAS and ICP-AES); a review

Metals in wine occur at the mg l 1 level or less and, though not directly related to the taste of the final product, their content should be determined because excess is undesirable, and in some cases prohibited, due to potential toxicity. Lead content in wine, for example, is restricted in several states by legislation to guarantee consumer health protection. Of several methods for metal determination, techniques of atomic spectroscopy are the most sensitive and rapid. Most of the elements present in wine can be determined with these techniques, at concentrations ranging from the mg l -1 to the μg l -1 level. Here, inductively coupled plasma-atomic emission spectrometry (ICP-AES), flame atomic absorption spectrometry (flame-AAS) and graphite furnace-atomic absorption spectrometry (GF-AAS) are compared for their characteristics as employed in metal determination in wine.     

Explicit geometric model of a radioscopic imaging system

In this paper a new explicit model of a radioscopic imaging system is presented. The model includes three parts: X-ray projection, image intensifier and CCD camera. The X-ray projection is modelled as a linear central projection. The image intensifier is modelled using a new explicit model that considers the curved input screen and the non-linear projection caused by electromagnetic fields. The CCD camera is modelled according to a general 2D projective transformation of the output screen of the image intensifier. Thus, the back-projection, required for 3D reconstruction, can be expressed in a closed-form. The model is compared to seven other known models. The presented model achieves high accuracy.     

Classification of Potato Chips Using Pattern Recognition

ABSTRACT: An approach to classify potato chips using pattern recognition from color digital images consists of 5 steps: (1) image acquisition, (2) preprocessing, (3) segmentation, (4) feature extraction, and (5) classification. Ten chips prepared for each of the following 6 conditions were examined: 2 pretreatments (blanched and unblanched) at 3 temperatures (120°C, 150°C, and 180°C). More than 1500 features were extracted from each of the 60 images. Finally, 11 features were selected according to their classification attributes. Seven different classification cases (for example, classification of the 6 classes or distinction between blanched and unblanched samples) were analyzed using the selected features. Although samples were highly heterogeneous, using a simple classifier and a small number of features, it was possible to obtain a good performance value in all cases: classification of the 6 classes was in the confidence interval between 78% and 89% with a probability of 95%.     

Chitosan and cyanoguanidine-crosslinked chitosan coated glass beads and its application in fixed bed adsorption

Abstract

The aim of this work was to coat glass beads with chitosan and cyanoguanidine-crosslinked chitosan and, afterward, to verify the potential of the coated beads as adsorbent for the FD&C Red 40 dye in fixed bed column. The coating was realized in eight experimental conditions, varying the technique (dip coating or casting) and the coating solution composition (different proportions of chitosan, cyanoguanidine, glycerol, sorbitol, sodium alginate, NaOH and CaCl₂). The beads coated in the eight experimental conditions were then applied to adsorb FD&C Red 40 in a fixed bed column, from aqueous solutions at pH 3.0 and 6.0. The breakthrough curves were fitted according to Thomas and Yoon-Nelson dynamic models. The coating was dependent of the solution composition. The casting technique presented coating percentage values of 92%, while the dip coating technique presented only 43%. The breakthrough curves revealed that the use of glass beads coated with cyanoguanidine-crosslinked chitosan had good performance, attaining adsorption capacity values higher than 80?mg g^?1. Thomas and Yoon-Nelson models were able to predict the breakthrough curves in all conditions.     

Development of a computer vision system to measure the color of potato chips

The objective of this research was to design and implement an inexpensive computer vision system for measuring the color of a highly heterogeneous food material not only in shape as well in color such as potato chips in L^*a^*b^* units from RGB images. The system was composed of (i) a digital color camera for acquiring the images in a digital format, (ii) a computer for storage the images, (c) image analysis routines integrated into a software programmed in Matlab that converts the color RGB of the food image into L^*a^*b^* units. In this way the color of potato chips can be calculated in L^*a^*b^* units over representative areas and in a reproducible way. The kinetics of color changes in potato slices during frying at four temperatures was followed using the implemented computer vision system (CVS). Color values in L^*a^*b^* units were recorded at different sampling times during frying at the four oil temperatures using the total color change parameter (ΔE). Chips fried at higher temperatures get darker as expected and showed by the CVS. The implemented computer vision system can be used to study as well foods different from potato chips by selecting their proper settings for image acquisition and digital image processing.     

Antioxidant enzyme activity and hydrogen peroxide content during the drying of Arabica coffee beans

The development of the germination process and drought stress during the drying of coffee can generate reactive oxygen species, which can be neutralized by way of antioxidant mechanisms. No studies related to antioxidant enzymes during the drying of coffee were found in the literature, and considering their importance, the enzymatic activities of superoxide dismutase (SOD), guaiacol peroxidase (GPOX) and glutathione reductase (GR), and also the hydrogen peroxide content were evaluated during the drying of two types of coffee bean, one processed as natural coffee and the other as pulped natural coffee. The results showed a reduction in the SOD, GPOX and GR enzymatic activities of the natural coffee as compared to the pulped natural coffee during the drying period. Moreover, the hydrogen peroxide content of the natural coffee was greater than that of the pulped natural coffee. These results suggest the development of oxidative stress during the coffee drying process, controlled more efficiently in pulped natural coffee by the early action of GPOX during the drying process. Nevertheless, differential responses by SOD isoenzymes and possibly the role of other peroxidases also appear to be involved in the responses observed.     

Robust automated multiple view inspection

Recently, Automated Multiple View Inspection (AMVI) has been developed for automated defect detection of manufactured objects, and the framework was successfully implemented for calibrated image sequences. However, it is not easy to be implemented in industrial environments because the calibration is a difficult and an unstable process. To overcome these disadvantages, the robust AMVI strategy, which assumes that an unknown affine transformation exists between each pair of uncalibrated images, is proposed. This transformation is estimated using two complementary robust procedures: a global approximation of the affine mapping is computed by creating candidate correspondences via B-splines and selecting those which better satisfy the epipolar constraint for uncalibrated images. Then, we use this approximation as initial estimate of a robust intensity-based matching approach, which is applied locally on each potential defect. The result is that false alarms are discarded, and the defects of an industrial object are actually tracked along the uncalibrated image sequence. The method is successful as shown in our experiments on aluminum die castings.