Understanding and measuring the urban pervasive infrastructure

The increasing popularity of mobile computing devices has allowed for new research and application areas. Specifically, urban areas exhibit an elevated concentration of such devices enabling potential ad-hoc co-operation and sharing of resources among citizens. Here, we argue that people, architecture and technology together provide the infrastructure for these applications and an understanding of this infrastructure is important for effective design and development. We focus on describing the metrics for describing this infrastructure and elaborate on a set of observation, analysis and simulation methods for capturing, deriving and utilising those metrics.

Can we do without GUIs? Gesture and speech interaction with a patient information system

We have developed a gesture input system that provides a common interaction technique across mobile, wearable and ubiquitous computing devices of diverse form factors. In this paper, we combine our gestural input technique with speech output and test whether or not the absence of a visual display impairs usability in this kind of multimodal interaction. This is of particular relevance to mobile, wearable and ubiquitous systems where visual displays may be restricted or unavailable. We conducted the evaluation using a prototype for a system combining gesture input and speech output to provide information to patients in a hospital Accident and Emergency Department. A group of participants was instructed to access various services using gestural inputs. The services were delivered by automated speech output. Throughout their tasks, these participants could see a visual display on which a GUI presented the available services and their corresponding gestures. Another group of participants performed the same tasks but without this visual display. It was predicted that the participants without the visual display would make more incorrect gestures and take longer to perform correct gestures than the participants with the visual display. We found no significant difference in the number of incorrect gestures made. We also found that participants with the visual display took longer than participants without it. It was suggested that for a small set of semantically distinct services with memorable and distinct gestures, the absence of a GUI visual display does not impair the usability of a system with gesture input and speech output.     

Globally Convergent Modification of the Quickprop Method

A mathematical framework for the convergence analysis of the well-known Quickprop method is described. Furthermore, we propose a modification of this method that exhibits improved convergence speed and stability, and, at the same time, alleviates the use of heuristic learning parameters. Simulations are conducted to compare and evaluate the performance of the new modified Quickprop algorithm with various popular training algorithms. The results of the experiments indicate that the increased convergence rates achieved by the proposed algorithm, affect by no means its generalization capability and stability.     

Detecting objects of variable shape structure with hidden state shape models

This paper proposes a method for detecting object classes that exhibit variable shape structure in heavily cluttered images. The term "variable shape structure" is used to characterize object classes in which some shape parts can be repeated an arbitrary number of times, some parts can be optional, and some parts can have several alternative appearances. Hidden State Shape Models (HSSMs), a generalization of Hidden Markov Models (HMMs), are introduced to model object classes of variable shape structure using a probabilistic framework. A polynomial inference algorithm automatically determines object location, orientation, scale and structure by finding the globally optimal registration of model states with the image features, even in the presence of clutter. Experiments with real images demonstrate that the proposed method can localize objects of variable shape structure with high accuracy. For the task of hand shape localization and structure identification, the proposed method is significantly more accurate than previously proposed methods based on chamfer-distance matching. Furthermore, by integrating simple temporal constraints, the proposed method gains speed-ups of more than an order of magnitude, and produces highly accurate results in experiments on non-rigid hand motion tracking.     

Scanned images resolution improvement using neural networks

A novel method of improving the spatial resolution of scanned images, by means of neural networks, is presented in this paper. Images of different resolution, originating from scanner, successively train a neural network, which learns to improve resolution from 25 to 50 pixels-per-inch (ppi), then from 100 to 200 ppi and finally, from 50 to 100 ppi. Thus, the network is provided with consistent knowledge regarding the point spread function (PSF) of the scanner, whilst it gains the generalization ability to reconstruct finer resolution images unfamiliar to it. The novelty of the proposed image-resolution-enhancement technique lies in the successive training of the neural structure with images of increasing resolution. Comparisons with the image scanned at 400 ppi demonstrate the superiority of our method to conventional interpolation techniques.     

Neuro-control of unmanned underwater vehicles

Unmanned underwater vehicles (UUVs) typically operate in uncertain and changing environments. Since the dynamics of UUVs are highly nonlinear and their hydrodynamic coefficients vary with different operating conditions, a high-performance control system of a UUV is needed to have the capacities of learning and adaptation to the variations in the UUV's dynamics. This paper presents the utilization of an adaptive neuro-control scheme as a controller for controlling a UUV in six degrees of freedom. No prior offline training phase and no explicit knowledge of the structure of the vehicle are required, and the proposed scheme exploits the advantages of both neural network control and adaptive control. Asymptotic convergence of the UUV's tracking errors and stability of the presented control system is guaranteed on the basis of the Lyapunov theory. In this paper, neural network architectures based on radial basis functions and multilayer structures have been used to evaluate the performance of the adaptive controller via computer simulation.     

Perceived Autonomy Support in Physical Education and Leisure-Time Physical Activity: A Cross-Cultural Evaluation of the Trans-Contextual Model.

This study tested the replicability and cross-cultural invariance of a trans-contextual model of motivation across 4 samples from diverse cultures. The model proposes a motivational sequence in which perceived autonomy support (PAS) in physical education (PE) predicts autonomous motivation, intentions, and behavior in a leisure-time (LT) physical activity context. High-school pupils from Britain, Greece, Poland, and Singapore completed measures of PAS and autonomous motives in a PE context. Goodfitting path-analytic models supported the main hypotheses of the trans-contextual model in the British, Greek, and Singaporean samples. PAS in PE had significant total effects on autonomous motives in LT, except in the Polish sample. The effect of autonomous motives in LT on physical activity intentions and behavior was mediated by theory of planned behavior constructs in all samples. Results supported the main hypotheses of the trans-contextual model across cultures, although the effect of PAS was not pervasive in the Polish sample. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A Comparison of Word- and Sense-Based Text Categorization Using Several Classification Algorithms

Most of the text categorization algorithms in the literature represent documents as collections of words. An alternative which has not been sufficiently explored is the use of word meanings, also known as senses. In this paper, using several algorithms, we compare the categorization accuracy of classifiers based on words to that of classifiers based on senses. The document collection on which this comparison takes place is a subset of the annotated Brown Corpus semantic concordance. A series of experiments indicates that the use of senses does not result in any significant categorization improvement.     

Impact of Approximation Error on the Decisions of LDPC Decoding

In this paper the impact of the approximation error on the decisions taken by LDPC decoders is studied. In particular, we analyze the mechanism, by means of which approximation error alters the decisions of a finite-word-length implementation of the decoding algorithm, with respect to the decisions taken by the infinite precision case, approximated here by double-precision floating-point. We focus on four popular algorithms for LDPC decoding, namely Log Sum-Product, Min-Sum, normalized Min-Sum and offset Min-Sum. A corresponding theoretical model is developed which derives an expression for the probability of altering the decision due to approximation. The model is applied to the above algorithms for the case of the first iteration as well as for higher numbers of iterations. Finally, experimental results prove the validity of the proposed model.