Abstract Like all the major upwelling regions, the Canary Current is characterised by intense mesoscale structure in the transition zone between the cool, nutrient-rich waters of the coastal upwelling regime and the warmer, oligotrophic waters of the open ocean. The Canary Island archipelago, which straddles the transition, introduces a second source of variability by perturbing the general southwestward flow of both ocean currents and Trade winds. The combined effects of the flow disturbance and the eddying and meandering of the boundary between upwelled and oceanic waters produce a complex pattern of regional variability. On the basis of historical data and a series of interdisciplinary field studies, the principal features of the region are described. These include a prominent upwelling filament originating near 28°N off the African coast, cyclonic and anti-cyclonic eddies downstream of the archipelago, and warm wake regions protected from the Trade winds by the high volcanic peaks of the islands. The filament is shown to be a recurrent feature, apparently arising from the interaction of a topographically trapped cyclonic eddy with the outer edge of the coastal upwelling zone. Its role in the transport and exchange of biogenic material, including fish larvae, is considered. Strong cyclonic eddies, observed throughout the year, drift slowly southwestward from Gran Canaria. One sampled in late summer was characterised by large vertical isopycnal displacements, apparent surface divergence and strong upwelling, producing a fourfold increase in chlorophyll concentrations over background values. Such intense eddies can be responsible for a major contribution to the vertical flux of nitrogen. The lee region of Gran Canaria is shown to be a location of strong pycnocline deformation resulting from Ekman pumping on the wind shear boundaries, which may contribute to the eddy formation process.

The transition zone of the Canary Current upwelling region

Geographic Object-Based Image Analysis (GEOBIA) represents the most innovative new trend for processing remote sensing images that has appeared during the last decade. However, its application is mainly based on expert knowledge, which consequently highlights important scientific issues with respect to the robustness of the methods applied in GEOBIA. In this paper, we argue that GEOBIA would benefit from another technical enhancement involving knowledge representation techniques such as ontologies. Although the role of ontologies in Geographical Information Sciences (GISciences) is not a new topic, few works have discussed how ontologies, considered from the perspective of a remote sensing specialist, can contribute to advancing remote sensing science. We summarize the main applications of ontologies in GEOBIA, especially for data discovery, automatic image interpretation, data interoperability, workflow management and data publication. Finally, we discuss the major issues related to the construction of ontologies suitable for remote sensing applications and outline long-term future advances that can be expected for the remote sensing community.

Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective

The web contains a huge volume of data, and it's populating every moment to the point that human beings cannot deal with the vast amount of data manually or via traditional tools. Hence an advanced tool is required to filter such massive data and mine the valuable information. Recommender systems are among the most excellent tools for such a purpose in which collaborative filtering is widely used. Collaborative filtering (CF) has been extensively utilized to offer personalized recommendations in electronic business and social network websites. In that, matrix factorization is an efficient technique; however, it depends on past transactions of the users. Hence, there will be a data sparsity problem. Another issue with the collaborative filtering method is the cold start issue, which is due to the deficient information about new entities. A novel method is proposed to overcome the data sparsity and the cold start problem in CF. For cold start issue, Recommender System with Linked Open Data (RS-LOD) model is designed and for data sparsity problem, Matrix Factorization model with Linked Open Data is developed (MF-LOD). A LOD knowledge base “DBpedia” is used to find enough information about new entities for a cold start issue, and an improvement is made on the matrix factorization model to handle data sparsity. Experiments were done on Netflix and MovieLens datasets show that our proposed techniques are superior to other existing methods, which mean recommendation accuracy is improved.

Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data

Postgraduate Students: Vladimír Arnot, Daniel Čapek, Rudolf Čejka, Dao Minh, Tomá Dulík, Martin Hrubý, Radek Kočí, Petr Kotásek, Marek Křejpský, Bohuslav Křena, Vladislav Kubíček, Vladimír Marek, Petr Matouek, Ale Mičín, Jiří Očenáek, Tomá Ondráček, Petr Peňás, Jaroslav Ráb, Richard Růička, Luká Sekanina, Ivan Schwarz, Azedien Sllame, Petr Smolík, Jiří Staroba, Josef Strnadel, Luká Szemla, Pavel Tinovský, Michal Tomů, Milan Urbáek, Michal Vojkůvka, Petr Vurm, Frantiek Zbořil

Department of Computer Science and Engineering

Satellite image classification process involves grouping the image pixel values into meaningful categories Several satellite image classification methods and techniques are available Satellite image classification methods can be broadly classified into three categories 1) automatic 2) manual and 3) hybrid All three methods have their own advantages and disadvantages Majority of the satellite image classification methods fall under first category Satellite image classification needs selection of appropriate classification method based on the requirements The current research work is a study on satellite image classification methods and techniques The research work also compares various researcher’s comparative results on satellite image classification methods

https://research.ijcaonline.org/volume119/number8/pxc3903779.pdf

Satellite Image Classification Methods and Techniques: A Review

One of the key tools of the unified modelling language for behaviour modelling is the use-case model. The behaviour of a use case can be described by means of interaction diagrams (sequence and collaboration diagrams), activity charts and state diagrams or by pre-conditions and post-conditions, as well as natural language text, where appropriate. This article explains a technique to describe use cases by means of sequence diagrams. It compares sequence diagrams in order to define sequence-diagram relationships for identifying and defining use-case relationships. This article uses an automatic teller machine system case study to illustrate our proposal.

/pdf/describing-use-case-relationships-with-sequence-diagrams-44ggehxt4q.pdf

Describing Use-Case Relationships with Sequence Diagrams

In this paper we present a framework for ocean image classification based on ontologies. With this aim, we will describe how low and high level content of ocean satellite images can be modeled with an ontology. In addition, we will show how the image classification can be modeled with the ontology in which decision tree based classifiers and rule-based expert systems are represented. Particularly, the rule based expert systems include rules about low-level features (called training and labeling rules), and rules defined from the labeling (called human expert rules). The modeling with the ontology provides an extensible framework in which accommodate several methods of image classification. One of the main aims of our proposal is to provide a mechanism to share data about image classification between applications. We have developed an extensible Protege plugin to classify images.

A Framework for Ocean Satellite Image Classification Based on Ontologies

In this paper we present how to develop graphical user interfaces from two UML models: use case and activity diagrams. Our method obtains from them a UML class diagram for representing GUI components, and it is suitable for generating code fragments, which can be considered as GUI prototypes.

/pdf/designing-gui-components-from-uml-use-cases-546zevqu6q.pdf

Designing GUI components from UML use cases

http://indalog.ual.es/WWW/infscience07.pdf

A performance comparison of distance-based query algorithms using R-trees in spatial databases

The Model-Driven Development (MDD) describes and maintains models of the system under development. The Unified Modeling Language (UML) supports a set of semantics and notation that addresses all scales of architectural complexity by using a MDD perspective. Use Cases and Activity Charts are two modeling techniques of the UML. The first one helps the designers to identify the requirements of the system discovering its high level functionality. The second one helps them to specify the internal behaviour of a certain entity or subsystem of the software developed, such as a database, a graphical interface, a software component, or any specific software. However, there is not a direct way to relate/model the requirements (use cases) with their internal behavior (activity charts). In this paper we present a method for describing use cases with activity charts. Our technique also allow us to identify the two main use case relationships —include and generalization— by means of activity charts. As a case study, we will show how to use the activity charts to describe graphical user interfaces (GUI) from use cases. In particular, we will show an Internet book shopping system example.

Jesús M. Almendros-Jiménez

Papers

Describing Use-Case Relationships with Sequence Diagrams

A Framework for Ocean Satellite Image Classification Based on Ontologies

Designing GUI components from UML use cases

A performance comparison of distance-based query algorithms using R-trees in spatial databases

Describing use cases with activity charts