Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Recent progress in extrusion 3D bioprinting of hydrogel biomaterials for tissue regeneration: a comprehensive review with a focus on advanced fabrication techniques

Over the last decade, 3D bioprinting has received immense attention from research communities for developing functional tissues. Thanks to the complexity of tissues, various bioprinting methods are exploited to figure out the challenges of tissue fabrication, in which hydrogels are widely adopted as a bioink in cell printing technologies based on the extrusion principle. Thus far, there is a wealth of the literature proposing the crucial parameters of extrusion-based bioprinting of hydrogel biomaterials (e.g., hydrogel properties, printing conditions, and tissue scaffold design) toward enhancing performance. Despite the growing research in this field, numerous challenges that hinder advanced applications still exist. Herein, the most recently reported hydrogel-based bioprinted scaffolds, i.e., skin, bone, cartilage, vascular, neural, and muscular (including skeletal, cardiac, and smooth), are systematically discussed with an emphasis on the advanced fabrication techniques from tissue engineering perspective. Methods covered include the multiple-dispenser, coaxial, and hybrid 3D bioprinting. The present work is a unique study to figure out the opportunities of the novel techniques to fabricate complicated constructs with structural and functional heterogeneity. Finally, the principal challenges of current studies and a vision of future research are presented

Holistic Resilience Quantification Framework of Rural Communities

Communities need to prepare for anticipated hazards, adapt to varying conditions, and resist and recover rapidly from disturbances. Protecting the built environment from natural and man-made hazards and understanding the impact of these hazards helps allocate resources efficiently. Recently, an indicator-based and time-dependent approach was developed for defining and measuring the functionality and disaster resilience continuously at the community level. This computational method uses seven dimensions that find qualitative characteristics and transforms them into quantitative measures. The proposed framework is used to study the resilience of rural communities’ subject to severe flooding events. Harlan County in the Appalachian region is chosen as a case study to evaluate the proposed resilience quantification framework subject to severe flooding. The results show the validity of the proposed approach as a decision-support mechanism to assess and enhance the resilience of rural communities

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

Machine Learning Meets Advanced Robotic Manipulation

Automated industries lead to high quality production, lower manufacturing cost and better utilization of human resources. Robotic manipulator arms have major role in the automation process. However, for complex manipulation tasks, hard coding efficient and safe trajectories is challenging and time consuming. Machine learning methods have the potential to learn such controllers based on expert demonstrations. Despite promising advances, better approaches must be developed to improve safety, reliability, and efficiency of ML methods in both training and deployment phases. This survey aims to review cutting edge technologies and recent trends on ML methods applied to real-world manipulation tasks. After reviewing the related background on ML, the rest of the paper is devoted to ML applications in different domains such as industry, healthcare, agriculture, space, military, and search and rescue. The paper is closed with important research directions for future works

Artificial Intelligence in the development of modern infrastructures

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform tasks as human beings. Most of the examples of AI you hear about today - from computers playing chess to autonomous driving cars - rely heavily on deep learning and natural language processing

Development and Validation of Characterization Method Using Finite Element Numerical Modeling and Advance Laboratory Methods for Western Australia Asphalt Mixes

A quantitative understanding of materials characterization of asphalt mixtures is required to determine the appropriate asphalt mixtures so that pavement distress and deformation can be reduced. Various numerical modeling and laboratory tools are used to assess the asphalt mixtures of flexible pavement and provided a significant and novel contribution to understanding of characterization of asphalt mixtures and design of flexible pavement. The contribution to understanding from this thesis shall be integrated in future body of knowledge