Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

GIS based models for optimisation of marine cage aquaculture in Tenerife, Canary Islands

This study focused on the optimisation of offshore marine fish-cage farming in Tenerife, Canary Islands. The main objective was to select the most suitable sites for offshore cage culture. This is a key factor in any aquaculture operation, affecting both success and sustainability. Moreover, it can solve conflicts between different coastal activities, making a rational use of the coastal space. Site selection was achieved by using Geographical Information Systems (GIS) based models and related technology, such as satellite images and Global Positioning System (GPS), to support the decision-making process. Three different cage systems were selected and proposed for different areas around Tenerife. Finally, a particulate waste distribution model (uneaten feed and faeces) was developed, also using GIS, for future prediction of the dispersive nature of selected sites. This can reduce the number of sites previously identified as most suitable, by predicting possible environmental impacts on the benthos if aquaculture was to be developed on a specific site. The framework for spatial multi-criteria decision analysis used in this study began with a recognition and definition of the decision problem. Subsequently, 31 production functions (factors and constraints) were identified, defined and subdivided into 8 sub-models. These sub-models were then integrated into a GIS database in the form of thematic layers and later scored for standardization. At this stage, the database was verified by field sampling to establish the quality of data used. The decision maker's preferences were incorporated into the decision model by assigning weights of relative importance to the evaluation under consideration. These, together with the thematic layers, were integrated by using Multi-criteria Evaluation (MCE) and simple overlays to provide an overall assessment of possible alternatives. Finally, sensitivity analysis was performed to determine the model robustness. The integration, manipulations and presentation of the results by means of GIS-based models in this sequential and logical flow of steps proved to be very effective for helping the decision-making process of site selection in study. On the whole, this study revealed the usefulness of GIS as an aquaculture planning and management tool. Cage systems that can withstand harsh environments were found to be suitable for use over a broader area of Tenerife's coastline. Thus, the more robust self-tensioned cage (SeaStation®) could be used over a greater area than the weaker gravity cages (Corelsa®). From the 228 km2 of available area for siting cages in the coastal regions with depth of 50 m, the suitable area (sum of scores 6, 7 and 8) for siting SeaStation® cages was 61 km2, while the suitable area for SeaStation® and Corelsa® cages was 49 and 37 km2 respectively. Most of the variation between these three cage systems was found among the intermediate suitability scores. It was concluded that the biggest differences in suitable area among cage systems are between Corelsa® and SeaStation® systems, followed by differences between Corelsa® and OceanSpar® cages, and OceanSpar® and SeaStation® respectively. This variability was mostly located on the N and NNW of the island, where waves, both long and short-term, are higher