A reconfigurable hybrid intelligent system for robot navigation

Soft computing has come of age to o er us a wide array of powerful and e cient algorithms that independently matured and in uenced our approach to solving problems in robotics, search and optimisation. The steady progress of technology, however, induced a ux of new real-world applications that demand for more robust and adaptive computational paradigms, tailored speci cally for the problem domain. This gave rise to hybrid intelligent systems, and to name a few of the successful ones, we have the integration of fuzzy logic, genetic algorithms and neural networks. As noted in the literature, they are signi cantly more powerful than individual algorithms, and therefore have been the subject of research activities in the past decades. There are problems, however, that have not succumbed to traditional hybridisation approaches, pushing the limits of current intelligent systems design, questioning their solutions of a guarantee of optimality, real-time execution and self-calibration. This work presents an improved hybrid solution to the problem of integrated dynamic target pursuit and obstacle avoidance, comprising of a cascade of fuzzy logic systems, genetic algorithm, the A* search algorithm and the Voronoi diagram generation algorithm

Face tracking using a hyperbolic catadioptric omnidirectional system

In the first part of this paper, we present a brief review on catadioptric omnidirectional systems. The special case of the hyperbolic omnidirectional system is analysed in depth. The literature shows that a hyperboloidal mirror has two clear advantages over alternative geometries. Firstly, a hyperboloidal mirror has a single projection centre [1]. Secondly, the image resolution is uniformly distributed along the mirror’s radius [2]. In the second part of this paper we show empirical results for the detection and tracking of faces from the omnidirectional images using Viola-Jones method. Both panoramic and perspective projections, extracted from the omnidirectional image, were used for that purpose. The omnidirectional image size was 480x480 pixels, in greyscale. The tracking method used regions of interest (ROIs) set as the result of the detections of faces from a panoramic projection of the image. In order to avoid losing or duplicating detections, the panoramic projection was extended horizontally. Duplications were eliminated based on the ROIs established by previous detections. After a confirmed detection, faces were tracked from perspective projections (which are called virtual cameras), each one associated with a particular face. The zoom, pan and tilt of each virtual camera was determined by the ROIs previously computed on the panoramic image. The results show that, when using a careful combination of the two projections, good frame rates can be achieved in the task of tracking faces reliably

Accelerated face detector training using the PSL framework

We train a face detection system using the PSL framework [1] which combines the AdaBoost learning algorithm and Haar-like features. We demonstrate the ability of this framework to overcome some of the challenges inherent in training classifiers that are structured in cascades of boosted ensembles (CoBE). The PSL classifiers are compared to the Viola-Jones type cas- caded classifiers. We establish the ability of the PSL framework to produce classifiers in a complex domain in significantly reduced time frame. They also comprise of fewer boosted en- sembles albeit at a price of increased false detection rates on our test dataset. We also report on results from a more diverse number of experiments carried out on the PSL framework in order to shed more insight into the effects of variations in its adjustable training parameters

A novel bootstrapping method for positive datasets in cascades of boosted ensembles

We present a novel method for efficiently training a face detector using large positive datasets in a cascade of boosted ensembles. We extend the successful Viola-Jones [1] framework which achieved low false acceptance rates through bootstrapping negative samples with the capability to also bootstrap large positive datasets thereby capturing more in-class variation of the target object. We achieve this form of bootstrapping by way of an additional embedded cascade within each layer and term the new structure as the Bootstrapped Dual-Cascaded (BDC) framework. We demonstrate its ability to easily and efficiently train a classifier on large and complex face datasets which exhibit acute in-class variation

A last glacial ice sheet on the Pacific Russian coast and catastrophic change arising from coupled ice–volcanic interaction

Controversy exists over the extent of glaciation in Eastern Asia at the Last Glacial Maximum: complete ice sheet cover vs. restricted mountain icefields (an area discrepancy equivalent to 3.7 Greenland Ice Sheets). Current arguments favour the latter. However, significant last glacial ice-rafted debris (IRD) exists in NW Pacific ocean cores, which must have been sourced from a major ice sheet somewhere bordering the North Pacific. The origin of this IRD is addressed through a combination of marine core analysis, iceberg trajectory modelling and remote sensing of glacial geomorphology. We find compelling evidence for two stages of glaciation centred on the Kamchatka area of maritime southeast Russia during the last glacial, with ice extent intermediate in size between previous maximum and minimum reconstructions. Furthermore, a significant increase in iceberg flux precedes, and accompanies, a substantial marine core ash deposit at around 40ka BP. We speculate that rapid decay of the first stage of the ice sheet may have triggered substantial volcanic activity

A new 2D static hand gesture colour image dataset for ASL gestures

It usually takes a fusion of image processing and machine learning algorithms in order to build a fully-functioning computer vision system for hand gesture recognition. Fortunately, the complexity of developing such a system could be alleviated by treating the system as a collection of multiple sub-systems working together, in such a way that they can be dealt with in isolation. Machine learning need to feed on thousands of exemplars (e.g. images, features) to automatically establish some recognisable patterns for all possible classes (e.g. hand gestures) that applies to the problem domain. A good number of exemplars helps, but it is also important to note that the efficacy of these exemplars depends on the variability of illumination conditions, hand postures, angles of rotation, scaling and on the number of volunteers from whom the hand gesture images were taken. These exemplars are usually subjected to image processing first, to reduce the presence of noise and extract the important features from the images. These features serve as inputs to the machine learning system. Different sub-systems are integrated together to form a complete computer vision system for gesture recognition. The main contribution of this work is on the production of the exemplars. We discuss how a dataset of standard American Sign Language (ASL) hand gestures containing 2425 images from 5 individuals, with variations in lighting conditions and hand postures is generated with the aid of image processing techniques. A minor contribution is given in the form of a specific feature extraction method called moment invariants, for which the computation method and the values are furnished with the dataset

The Geography of Internet Adoption by Retailers

Up till now, the literature on Internet adoption by retailers paid little attention to spatial variables. Using data on 27,000 retail outlets in the Netherlands, we investigate the geographical diffusion of Internet adoption by Dutch retailers. More precise, we examine to what extent retail Internet adoption differs between shopping centers, cities, and regions, while controlling for product and organizational variables. Results of the linear and multinomial logistic regressions suggest that shops at city centers are more likely to adopt the Internet than shops located at shopping centers at the bottom of the retail hierarchy. Furthermore, shops in large cities have a higher probability to adopt the Internet than shops in small cities. On the regional level, the likelihood of Internet adoption is higher for shops in core regions than for retail outlets in the periphery. In conclusion, geography seems to matter for retail Internet adoption.evolutionary economics, internet adoption, retailing

Conformance relations for distributed testing based on CSP

Copyright @ 2011 Springer Berlin HeidelbergCSP is a well established process algebra that provides comprehensive theoretical and practical support for refinement-based design and verification of systems. Recently, a testing theory for CSP has also been presented. In this paper, we explore the problem of testing from a CSP specification when observations are made by a set of distributed testers. We build on previous work on input-output transition systems, but the use of CSP leads to significant differences, since some of its conformance (refinement) relations consider failures as well as traces. In addition, we allow events to be observed by more than one tester. We show how the CSP notions of refinement can be adapted to distributed testing. We consider two contexts: when the testers are entirely independent and when they can cooperate. Finally, we give some preliminary results on test-case generation and the use of coordination messages. © 2011 IFIP International Federation for Information Processing

Levitation of quantum Hall critical states in a lattice model with spatially correlated disorder

The fate of the current carrying states of a quantum Hall system is considered in the situation when the disorder strength is increased and the transition from the quantum Hall liquid to the Hall insulator takes place. We investigate a two-dimensional lattice model with spatially correlated disorder potentials and calculate the density of states and the localization length either by using a recursive Green function method or by direct diagonalization in connection with the procedure of level statistics. From the knowledge of the energy and disorder dependence of the localization length and the density of states (DOS) of the corresponding Landau bands, the movement of the current carrying states in the disorder--energy and disorder--filling-factor plane can be traced by tuning the disorder strength. We show results for all sub-bands, particularly the traces of the Chern and anti-Chern states as well as the peak positions of the DOS. For small disorder strength $W$ we recover the well known weak levitation of the critical states, but we also reveal, for larger $W$, the strong levitation of these states across the Landau gaps without merging. We find the behavior to be similar for exponentially, Gaussian, and Lorentzian correlated disorder potentials. Our study resolves the discrepancies of previously published work in demonstrating the conflicting results to be only special cases of a general lattice model with spatially correlated disorder potentials. To test whether the mixing between consecutive Landau bands is the origin of the observed floating, we truncate the Hilbert space of our model Hamiltonian and calculate the behavior of the current carrying states under these restricted conditions.Comment: 10 pages, incl. 13 figures, accepted for publication in PR