The Reappearing Computer: the past and future of computing in design research

This paper investigates the early history of computing in design and in design research, focusing on individuals who were associated with the Department of Design Research at the Royal College of Art between the 1960s and the 1980s. The authors suggest that the theory and practice developed at that time may be valuable in thinking about the future, particularly when considering how computing may be used, in various forms, by designers in their work. A taxonomy of some early ideas and activities is presented which, it is suggested, displays a different emphasis from the way computing in design is conceived now. It is argued that as computing has become absorbed into mainstream culture, it has tended to “disappear” and its special qualities have become lost since it is regarded as “just a tool” like any other. A contrast is presented between this model of computing focused on facilitating or replacing hand-work and earlier models which prioritised computing’s relation to the mind. The authors note that some other fields seem currently to be reengaging with the idea of computing as something that is not quite like other tools. The article concludes with a list of questions addressed to the design and design research communities based on the authors’ analysis

Nuclear Plants and Emergency Virtual Simulations Based on a Low-Cost Engine Reuse

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Nuclear Plants and Emergency Virtual Simulations based on a Low-cost Engine Reuse

Our industrialised society comprises many industrial processes that are very important for everyone, in a wide range of fields. Activities related to these industrial processes, though, involve, in higher or lower degrees, some risk for personnel,  besides risk for the general public in some cases. Therefore, efficient training programs and simulations are highly required, to improve the processes involved, increasing safety for people. To cite an example, nuclear plants pose high safety requirements in operational and maintenance routines, to keep plants in safe operation conditions and reduce personnel exposure to radiation dose. Besides operational and maintenance in nuclear plants, there are also other situations where efficient training is required, as in evacuation planning from buildings in emergency situations. Also, rescue tasks play similar role. These apply specially for nuclear sites. Another situation that requires efficient training is security, what has special meaning for plants that involve dangerous materials, such as nuclear plants. Nuclear materials must be kept under high security level, to avoid any misuse

A wireless sEMG-based body-machine interface for assistive technology devices

Assistive technology (AT) tools and appliances are being more and more widely used and developed worldwide to improve the autonomy of people living with disabilities and ease the interaction with their environment. This paper describes an intuitive and wireless surface electromyography (sEMG) based body-machine interface for AT tools. Spinal cord injuries at C5-C8 levels affect patients' arms, forearms, hands, and fingers control. Thus, using classical AT control interfaces (keypads, joysticks, etc.) is often difficult or impossible. The proposed system reads the AT users' residual functional capacities through their sEMG activity, and converts them into appropriate commands using a threshold-based control algorithm. It has proven to be suitable as a control alternative for assistive devices and has been tested with the JACO arm, an articulated assistive device of which the vocation is to help people living with upper-body disabilities in their daily life activities. The wireless prototype, the architecture of which is based on a 3-channel sEMG measurement system and a 915-MHz wireless transceiver built around a low-power microcontroller, uses low-cost off-the-shelf commercial components. The embedded controller is compared with JACO's regular joystick-based interface, using combinations of forearm, pectoral, masseter, and trapeze muscles. The measured index of performance values is 0.88, 0.51, and 0.41 bits/s, respectively, for correlation coefficients with the Fitt's model of 0.75, 0.85, and 0.67. These results demonstrate that the proposed controller offers an attractive alternative to conventional interfaces, such as joystick devices, for upper-body disabled people using ATs such as JACO

Hybrid ACO and SVM algorithm for pattern classification

Ant Colony Optimization (ACO) is a metaheuristic algorithm that can be used to solve a variety of combinatorial optimization problems. A new direction for ACO is to optimize continuous and mixed (discrete and continuous) variables. Support Vector Machine (SVM) is a pattern classification approach originated from statistical approaches. However, SVM suffers two main problems which include feature subset selection and parameter tuning. Most approaches related to tuning SVM parameters discretize the continuous value of the parameters which will give a negative effect on the classification performance. This study presents four algorithms for tuning the SVM parameters and selecting feature subset which improved SVM classification accuracy with smaller size of feature subset. This is achieved by performing the SVM parameters’ tuning and feature subset selection processes simultaneously. Hybridization algorithms between ACO and SVM techniques were proposed. The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. Ten benchmark datasets from University of California, Irvine, were used in the experiments to validate the performance of the proposed algorithms. Experimental results obtained from the proposed algorithms are better when compared with other approaches in terms of classification accuracy and size of the feature subset. The average classification accuracies for the ACOR-SVM, IACOR-SVM, ACOMV-R and IACOMV-R algorithms are 94.73%, 95.86%, 97.37% and 98.1% respectively. The average size of feature subset is eight for the ACOR-SVM and IACOR-SVM algorithms and four for the ACOMV-R and IACOMV-R algorithms. This study contributes to a new direction for ACO that can deal with continuous and mixed-variable ACO