Remote Access to a Prototyping Laboratory

There is a growing global demand for continuing adult higher education particularly in science and engineering subjects. New technologies are emerging which would enable the development of a Remote Access Laboratory for rapid prototyping of Artificial Intelligence, as a learning environment for mechatronic engineering, in which high precision electromechanical devices are designed to exhibit autonomous behaviour. Secondary research investigated the learning theories for a Remote Access Laboratory, and the current practices for distance learning, involving groupware in shared activity 'collaboratories'. Having determined that the laboratory would need a multi-user interactive environment architecture, with the requirement for adaptability to rapid developments,a distributed software architecture was selected. The laboratory design was subsequently argued to be best served by Intelligent Agents in a Multi-Agent system. The aims of the research were to establish the viability of a Remote Access Laboratory for mechatronic experimentation, and to evaluate the technologies required to implement such a laboratory environment for rapid prototyping. These were achieved by developing a novel user interface, based on a multi-functional screen layout, and a graphical specification facility to provide robotic navigation that is intuitive to use and does not require text-based programming. The research investigated the prototyping of robotic behaviour, which used Programming by Demonstration as an innovative technique to prototype robot navigation. The method of designing behaviours met an anticipated need to allow the robot to interact with an environment, to achieve goals under conditions of uncertainty, while requiring a level of abstraction in the behaviour design. The interface structured a composite of the designed behaviours into prototype Artificial Intelligence using a hierarchical behaviour architecture, which complied with the principles of Object Orientated programming. This was subsequently a new and original programming method to facilitate rapid prototyping of Artificial Intelligence design and structuring. Experimentation involved 20 participants attempting to accomplish a series of tasks which involved using the prototyped interface and an existing text-based robot programming system. The participants were profiled by their formal qualifications, knowledge and experience. The experimental data obtained were used to establish a comparative measure of the prototype interface success compared with an existing distance-learning, home experiment kit, in the form of a small controllable model vehicle. The data obtained provided strong evidence to support the hypothesis that a Programming by Demonstration based system for rapid prototyping is more flexible and easier to use than a previously existing distance learning text-based system. The Programming by Demonstration system showed great promise, being quicker for prototyping, and more intuitive. The learning interface design pioneered new techniques and technologies for rapid prototyping of Artificial Intelligence in a Mechatronics Remote Access Laboratory

Virtual laboratories for education in science, technology, and engineering: A review

Within education, concepts such as distance learning, and open universities, are now becoming more widely used for teaching and learning. However, due to the nature of the subject domain, the teaching of Science, Technology, and Engineering are still relatively behind when using new technological approaches (particularly for online distance learning). The reason for this discrepancy lies in the fact that these fields often require laboratory exercises to provide effective skill acquisition and hands-on experience. Often it is difficult to make these laboratories accessible for online access. Either the real lab needs to be enabled for remote access or it needs to be replicated as a fully software-based virtual lab. We argue for the latter concept since it offers some advantages over remotely controlled real labs, which will be elaborated further in this paper. We are now seeing new emerging technologies that can overcome some of the potential difficulties in this area. These include: computer graphics, augmented reality, computational dynamics, and virtual worlds. This paper summarizes the state of the art in virtual laboratories and virtual worlds in the fields of science, technology, and engineering. The main research activity in these fields is discussed but special emphasis is put on the field of robotics due to the maturity of this area within the virtual-education community. This is not a coincidence; starting from its widely multidisciplinary character, robotics is a perfect example where all the other fields of engineering and physics can contribute. Thus, the use of virtual labs for other scientific and non-robotic engineering uses can be seen to share many of the same learning processes. This can include supporting the introduction of new concepts as part of learning about science and technology, and introducing more general engineering knowledge, through to supporting more constructive (and collaborative) education and training activities in a more complex engineering topic such as robotics. The objective of this paper is to outline this problem space in more detail and to create a valuable source of information that can help to define the starting position for future research

New paradigm for macromolecular crystallography experiments at SSRL: automated crystal screening and remote data collection

Through the combination of robust mechanized experimental hardware and a flexible control system with an intuitive user interface, SSRL researchers have screened over 200 000 biological crystals for diffraction quality in an automated fashion. Three quarters of SSRL researchers are using these data-collection tools from remote locations

Managing big data experiments on smartphones

The explosive number of smartphones with ever growing sensing and computing capabilities have brought a paradigm shift to many traditional domains of the computing field. Re-programming smartphones and instrumenting them for application testing and data gathering at scale is currently a tedious and time-consuming process that poses significant logistical challenges. Next generation smartphone applications are expected to be much larger-scale and complex, demanding that these undergo evaluation and testing under different real-world datasets, devices and conditions. In this paper, we present an architecture for managing such large-scale data management experiments on real smartphones. We particularly present the building blocks of our architecture that encompassed smartphone sensor data collected by the crowd and organized in our big data repository. The given datasets can then be replayed on our testbed comprising of real and simulated smartphones accessible to developers through a web-based interface. We present the applicability of our architecture through a case study that involves the evaluation of individual components that are part of a complex indoor positioning system for smartphones, coined Anyplace, which we have developed over the years. The given study shows how our architecture allows us to derive novel insights into the performance of our algorithms and applications, by simplifying the management of large-scale data on smartphones

Internet Predictions

More than a dozen leading experts give their opinions on where the Internet is headed and where it will be in the next decade in terms of technology, policy, and applications. They cover topics ranging from the Internet of Things to climate change to the digital storage of the future. A summary of the articles is available in the Web extras section

The complex interaction between Global Production Networks, Digital Information Systems and International Knowledge Transfers

Traditionally many studies of knowledge in economics have focused on localized networks and intra-regional collaborations. However, the rising frequency by which firms collaborate within the context of global networks of production and innovation, the increasingly intricate divisions of labor involved and the extensive use of the Internet to facilitate interaction are all relatively novel trends that underline the importance of knowledge creation and flows across different locations. Focusing on this topic, the present chapter examines the complex interactions between global production networks (GPN), digital information systems (DIS) and knowledge transfers in information technology industries. It seeks to disentangle the various conduits through which different kinds of knowledge are transferred within such networks, and investigate how recent generations of DIS are affecting those knowledge transfers. The paper concludes that the dual expansion of GPN and DIS is adding new complexity to the practice of innovation: To access knowledge necessary for sustained creativity firms often have to link up with remote partners in GPN, but to be able to absorb and utilize this knowledge, they also frequently have to engage in local interactive learning processes. These local- global linkages - and the various skills necessary to operate them - are strongly interdependent, mutually reinforcing and critical for the development and maintenance of innovation-based competitiveness.

Tier-scalable reconnaissance: the challenge of sensor optimization, sensor deployment, sensor fusion, and sensor interoperability

Robotic reconnaissance operations are called for in extreme environments, not only those such as space, including planetary atmospheres, surfaces, and subsurfaces, but also in potentially hazardous or inaccessible operational areas on Earth, such as mine fields, battlefield environments, enemy occupied territories, terrorist infiltrated environments, or areas that have been exposed to biochemical agents or radiation. Real time reconnaissance enables the identification and characterization of transient events. A fundamentally new mission concept for tier-scalable reconnaissance of operational areas, originated by Fink et al., is aimed at replacing the engineering and safety constrained mission designs of the past. The tier-scalable paradigm integrates multi-tier (orbit atmosphere surface/subsurface) and multi-agent (satellite UAV/blimp surface/subsurface sensing platforms) hierarchical mission architectures, introducing not only mission redundancy and safety, but also enabling and optimizing intelligent, less constrained, and distributed reconnaissance in real time. Given the mass, size, and power constraints faced by such a multi-platform approach, this is an ideal application scenario for a diverse set of MEMS sensors. To support such mission architectures, a high degree of operational autonomy is required. Essential elements of such operational autonomy are: (1) automatic mapping of an operational area from different vantage points (including vehicle health monitoring); (2) automatic feature extraction and target/region-of-interest identification within the mapped operational area; and (3) automatic target prioritization for close-up examination. These requirements imply the optimal deployment of MEMS sensors and sensor platforms, sensor fusion, and sensor interoperability

Future bathroom: A study of user-centred design principles affecting usability, safety and satisfaction in bathrooms for people living with disabilities

Research and development work relating to assistive technology 2010-11 (Department of Health) Presented to Parliament pursuant to Section 22 of the Chronically Sick and Disabled Persons Act 197

Remote Access of an Autonomous Seed Sowing Robot in a Learning Factory

The supervision of food production systems is instrumental in the advancement of food security. Remote access and control provides unique capabilities to the supervision and operation of such systems, as well as interesting opportunities for students to access learning factory facilities remotely. Thus, the AllFactory at the University of Alberta provides a unique environment that allows for testing of highly robotized food production lines. This paper proposes the use of digital twin models to enable remote access to learning factory’s systems and combines distributed sensors and computer vision to visualize the systems' operational status and motions while also providing a remote learning environment. In this study, a digital twin of a robotic seed sowing system, consisting of a Dobot M1 robotic arms is developed and tested. The robot system aims to pick crop seeds using pneumatic actuators and finally place them correctly in rockwool, while several cameras monitor seed and plant growth. The development of this tool hopes to support the continuous use of learning factories even in complicated situations