Using QR codes to aid accessibility in a museum

Purpose – This paper describes visitors' reactions to using an Apple iPad or smartphone to follow trails in a museum by scanning QR codes and draws conclusions on the potential for this technology to help improve accessibility at low-cost. Design/methodology/approach – Activities were devised which involved visitors following trails around museum objects, each labelled with a QR code and symbolised text. Visitors scanned the QR codes using a mobile device which then showed more information about an object. Project-team members acted as participant-observers, engaging with visitors and noting how they used the system. Experiences from each activity fed into the design of the next. Findings – Some physical and technical problems with using QR codes can be overcome with the introduction of simple aids, particularly using movable object labels. A layered approach to information access is possible with the first layer comprising a label, the second a mobile-web enabled screen and the third choices of text, pictures, video and audio. Video was especially appealing to young people. The ability to repeatedly watch video or listen to audio seemed to be appreciated by visitors with learning disabilities. This approach can have low equipment-cost. However, maintaining the information behind labels and keeping-up with technological changes are on-going processes. Originality/value – Using QR codes on movable, symbolised object labels as part of a layered information system might help modestly-funded museums enhance their accessibility, particularly as visitors increasingly arrive with their own smartphones or tablets.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Givens rotation based fast backward elimination algorithm for RBF neural network pruning

A fast backward elimination algorithm is introduced based on a QR decomposition and Givens transformations to prune radial-basis-function networks. Nodes are sequentially removed using an increment of error variance criterion. The procedure is terminated by using a prediction risk criterion so as to obtain a model structure with good generalisation properties. The algorithm can be used to postprocess radial basis centres selected using a k-means routine and, in this mode, it provides a hybrid supervised centre selection approach.

A parallel quantum histogram architecture

A parallel formulation of an algorithm for the histogram computation of n data items using an on-the-fly data decomposition and a novel quantum-like representation (QR) is developed. The QR transformation separates multiple data read operations from multiple bin update operations thereby making it easier to bind data items into their corresponding histogram bins. Under this model the steps required to compute the histogram is n/s + t steps, where s is a speedup factor and t is associated with pipeline latency. Here, we show that an overall speedup factor, s, is available for up to an eightfold acceleration. Our evaluation also shows that each one of these cells requires less area/time complexity compared to similar proposals found in the literature.