Security Authentication using Phase-Encoded Nanoparticle Structures and Polarized Light

Phase encoded nano structures such as Quick Response (QR) codes made of metallic nanoparticles are suggested to be used in security and authentication applications. We present a polarimetric optical method able to authenticate random phase encoded QR codes. The system is illuminated using polarized light and the QR code is encoded using a phase-only random mask. Using classification algorithms it is possible to validate the QR code from the examination of the polarimetric signature of the speckle pattern. We used Kolmogorov-Smirnov statistical test and Support Vector Machine algorithms to authenticate the phase encoded QR codes using polarimetric signatures.

Textural classification of land cover using support vector machines: an empirical comparison with parametric, non parametric and hybrid classifiers in the Bolivian Amazon

Land cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims to establish an efficient classification approach to accurately map all broad land cover classes in a large, heterogeneous tropical area of Bolivia, as a basis for further studies (e.g., land cover-land use change). Specifically, we compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbour and four different support vector machines - SVM), and hybrid classifiers, using both hard and soft (fuzzy) accuracy assessments. In addition, we test whether the inclusion of a textural index (homogeneity) in the classifications improves their performance. We classified Landsat imagery for two dates corresponding to dry and wet seasons and found that non-parametric, and particularly SVM classifiers, outperformed both parametric and hybrid classifiers. We also found that the use of the homogeneity index along with reflectance bands significantly increased the overall accuracy of all the classifications, but particularly of SVM algorithms. We observed that improvements in producer’s and user’s accuracies through the inclusion of the homogeneity index were different depending on land cover classes. Earlygrowth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land cover classes were mapped with producer’s and user’s accuracies of around 90%. Our approach seems very well suited to accurately map land cover in tropical regions, thus having the potential to contribute to conservation initiatives, climate change mitigation schemes such as REDD+, and rural development policies.

Desenvolupament d'algorismes biològicament inspirats basats en Support Vector Machines

L'objectiu d'aquest projecte ha estat el desenvolupament d'algorismes biològicament inspirats per a l'olfacció artificial. Per a assolir-lo ens hem basat en el paradigma de les màquines amb suport vectorial. Hem construit algoritmes que imitaven els processos computacionals dels diferents sistemes que formen el sistema olfactiu dels insectes, especialment de la llagosta Schistocerca gregaria. Ens hem centrat en el lòbuls de les antenes, i en el cos fungiforme. El primer està considerat un dispositiu de codificació de les olors, que a partir de la resposta temporal dels receptors olfactius a les antenes genera un patró d'activació espaial i temporal. Quant al cos fungiforme es considera que la seva funció és la d'una memòria per als olors, així com un centre per a la integració multi-sensorial. El primer pas ha estat la construcció de models detallats dels dos sistemes. A continuació, hem utilitzat aquests models per a processar diferents tipus de senyals amb l'objectiu de abstraure els principis computacionals subjacents. Finalment, hem avaluat les capacitats d'aquests models abstractes, i els hem utilitzat per al processat de dades provinents de sensors de gasos. Els resultats mostren que el models abstractes tenen millor comportament front el soroll i més capacitat d'emmagatzematge de records que altres models més clàssics, com ara les memòries associatives de Hopfield o fins i tot en determinades circumstàncies que les mateixes Support Vector Machines.

Image-Based Coral Reef Classification and Thematic Mapping

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos

Approximation and support theorem for a wave equation in two space dimensions

We prove a characterization of the support of the law of the solution for a stochastic wave equation with two-dimensional space variable, driven by a noise white in time and correlated in space. The result is a consequence of an approximation theorem, in the convergence of probability, for equations obtained by smoothing the random noise. For some particular classes of coefficients, approximation in the Lp-norm for p¿1 is also proved.

Improving a leaves automatic recognition process using PCA

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used, and Principal Component Analysis (PCA) is applied in order to study which is the best number of components for the classification task, implemented by means of a Support Vector Machine (SVM) System. Obtained results are satisfactory, and compared with [4] our system improves the recognition success, diminishing the variance at the same time.

Using a blog to create and support a Community of Inquiry in Secondary Education

Understanding how blogs can support collaborative learning is a vital concern for researchers and teachers. This paper explores how blogs may be used to support Secondary Education students’ collaborative interaction and how such an interaction process can promote the creation of a Community of Inquiry to enhance critical thinking and meaningful learning. We designed, implemented and evaluated a science case-based project in which fifteen secondary students participated. Students worked in the science blogging project during 4 months. We asked students to be collaboratively engaged in purposeful critical discourse and reflection in their blogs in order to solve collectively science challenges and construct meaning about topics related to Astronomy and Space Sciences. Through student comments posted in the blog, our findings showed that the blog environment afforded the construction of a Community of Inquiry and therefore the creation of an effective online collaborative learning community. In student blog comments, the three presences for collaborative learning took place: cognitive, social, and teaching presence. Moreover, our research found a positive correlation among the three presences –cognitive, social and teaching– of the Community of Inquiry model with the level of learning obtained by the students. We discuss a series of issues that instructors should consider when blogs are incorporated into teaching and learning. We claim that embedded scaffolds to help students to argue and reason their comments in the blog are required to foster blog-supported collaborative learning.

Reducción del vector de características de reconocimiento facial

In this present work, we are proposing a characteristics reduction system for a facial biometric identification system, using transformed domains such as discrete cosine transformed (DCT) and discrete wavelets transformed (DWT) as parameterization; and Support Vector Machines (SVM) and Neural Network (NN) as classifiers. The size reduction has been done with Principal Component Analysis (PCA) and with Independent Component Analysis (ICA). This system presents a similar success results for both DWT-SVM system and DWT-PCA-SVM system, about 98%. The computational load is improved on training mode due to the decreasing of input’s size and less complexity of the classifier.

Classificació i reconeixement de vídeo

El principal objectiu d’aquest projecte és aconseguir classificar diferents vídeos d’esports segons la seva categoria. Els cercadors de text creen un vocabulari segons el significat de les diferents paraules per tal de poder identificar un document. En aquest projecte es va fer el mateix però mitjançant paraules visuals. Per exemple, es van intentar englobar com a una única paraula les diferents rodes que apareixien en els cotxes de rally. A partir de la freqüència amb què apareixien les paraules dels diferents grups dins d’una imatge vàrem crear histogrames de vocabulari que ens permetien tenir una descripció de la imatge. Per classificar un vídeo es van utilitzar els histogrames que descrivien els seus fotogrames. Com que cada histograma es podia considerar un vector de valors enters vàrem optar per utilitzar una màquina classificadora de vectors: una Support vector machine o SVM

Mejora diagnóstica de hepatopatías de afectación difusa mediante técnicas de inteligencia artificial

The automatic diagnostic discrimination is an application of artificial intelligence techniques that can solve clinical cases based on imaging. Diffuse liver diseases are diseases of wide prominence in the population and insidious course, yet early in its progression. Early and effective diagnosis is necessary because many of these diseases progress to cirrhosis and liver cancer. The usual technique of choice for accurate diagnosis is liver biopsy, an invasive and not without incompatibilities one. It is proposed in this project an alternative non-invasive and free of contraindications method based on liver ultrasonography. The images are digitized and then analyzed using statistical techniques and analysis of texture. The results are validated from the pathology report. Finally, we apply artificial intelligence techniques as Fuzzy k-Means or Support Vector Machines and compare its significance to the analysis Statistics and the report of the clinician. The results show that this technique is significantly valid and a promising alternative as a noninvasive diagnostic chronic liver disease from diffuse involvement. Artificial Intelligence classifying techniques significantly improve the diagnosing discrimination compared to other statistics.

Breaking up the writing process: how wikis can support understanding the composition and revision strategies of young writers

Understanding how best to support immature writers in the development of their understanding of the writing process is an important concern for researchers and teachers. Social technologies have become key features of leisure and work place writing, yet knowledge about how to design educational settings that take full advantage of the affordances of web 2.0 technologies to support early writing is scarce. This paper presents a small scale study that investigated how writing in a wiki environment might facilitate and support students’ use of composition and revision strategies. Our findings show that wiki can enlarge young writers’ experience of the process of composition and revision both through their own efforts and by observing the process in others. In this study students employed a wide range of types of revisions both surface and text based changes. These revisions took place during the process of composition as well as at the end. It is argued here that writing in a wiki not only provides young writers with experience of a mode of composition prevalent in the contemporary work environment, but breaks up the process of writing in a way that may support students’ understanding of the processes of composition and revision.

LSB Matching Steganalysis Based on Patterns of Pixel Differences and Random Embedding

Peer-reviewed

Innovation and absorptive capacity: What is the role of technological frontier?

This paper explores how absorptive capacity affects the innovative performance and productivity dynamics of Spanish firms. A firm’s efficiency levels are measured using two variables: the labour productivity and the Total Factor Productivity (TFP). The theoretical framework is based on the seminal contributions of Cohen and Levinthal (1989, 1990) regarding absorptive capacity; and the applied framework is based on the four-stage structural model proposed by Crépon, Duguet and Mairesse (1998) for setting the determinants of R&D, the effects of R&D activities on innovation outputs, and the impacts of innovation on firm productivity. The present study uses a twostage structural model. In the first stage, a probit estimation is used to investigate how the sources of R&D, the absorptive capacity and a vector of the firm’s individual features influence the firm’s likelihood of developing innovations in products or processes. In the second phase, a quantile regression is used to analyze the effect of R&D sources, absorptive capacity and firm characteristics on productivity. This method shows the elasticity of each exogenous variable on productivity according to the firms’ levels of efficiency, and thus allows us to distinguish between firms that are close to the technological frontier and those that are further away from it. We used extensive firm-level panel data from 5,575 firms for the 2004-2009 period. The results show that the internal absorptive capacity has a strong impact on the productivity of firms, whereas the role of external absorptive capacity differs according to nature of the each industry and according the distance of firms from the technological frontier. Key words: R&D sources, innovation strategies, absorptive capacity, technological distance, quantile regression.

AgShare : Building Community and Content with Multiple Partners

Michigan State University and OER Africa are creating a win-win collaboration of existing organizations for African publishing, localizing, and sharing of teaching and learning materials that fill critical resource gaps in African MSc agriculture curriculum. By the end of the 18-month planning and pilot initiative, African agriculture universities, faculty, students, researchers, NGO leaders, extension staff, and farmers will participate in building AgShare by demonstrating its benefits and outcomes and by building momentum and support for growth.