A combined Bayesian Markovian approach for behaviour recognition

Numerous techniques exist which can be used for the task of behavioural analysis and recognition. Common amongst these are Bayesian networks and Hidden Markov Models. Although these techniques are extremely powerful and well developed, both have important limitations. By fusing these techniques together to form Bayes-Markov chains, the advantages of both techniques can be preserved, while reducing their limitations. The Bayes-Markov technique forms the basis of a common, flexible framework for supplementing Markov chains with additional features. This results in improved user output, and aids in the rapid development of flexible and efficient behaviour recognition systems.

Robustness of mutual information to inter-subject variability for automatic artefact removal from EEG

The externally recorded electroencephalogram (EEG) is contaminated with signals that do not originate from the brain, collectively known as artefacts. Thus, EEG signals must be cleaned prior to any further analysis. In particular, if the EEG is to be used in online applications such as Brain-Computer Interfaces (BCIs) the removal of artefacts must be performed in an automatic manner. This paper investigates the robustness of Mutual Information based features to inter-subject variability for use in an automatic artefact removal system. The system is based on the separation of EEG recordings into independent components using a temporal ICA method, RADICAL, and the utilisation of a Support Vector Machine for classification of the components into EEG and artefact signals. High accuracy and robustness to inter-subject variability is achieved.

A camera based digit location and recognition system for garment tracking

Garment information tracking is required for clean room garment management. In this paper, we present a camera-based robust system with implementation of Optical Character Reconition (OCR) techniques to fulfill garment label recognition. In the system, a camera is used for image capturing; an adaptive thresholding algorithm is employed to generate binary images; Connected Component Labelling (CCL) is then adopted for object detection in the binary image as a part of finding the ROI (Region of Interest); Artificial Neural Networks (ANNs) with the BP (Back Propagation) learning algorithm are used for digit recognition; and finally the system is verified by a system database. The system has been tested. The results show that it is capable of coping with variance of lighting, digit twisting, background complexity, and font orientations. The system performance with association to the digit recognition rate has met the design requirement. It has achieved real-time and error-free garment information tracking during the testing.

A β-amino acid modified heptapeptide containing a designed recognition element disrupts fibrillization of the amyloid β-peptide

We study the complex formation of a peptide betaAbetaAKLVFF, previously developed by our group, with Abeta(1–42) in aqueous solution. Circular dichroism spectroscopy is used to probe the interactions between betaAbetaAKLVFF and Abeta(1–42), and to study the secondary structure of the species in solution. Thioflavin T fluorescence spectroscopy shows that the population of fibers is higher in betaAbetaAKLVFF/Abeta(1–42) mixtures compared to pure Abeta(1–42) solutions. TEM and cryo-TEM demonstrate that co-incubation of betaAbetaAKLVFF with Abeta(1–42) causes the formation of extended dense networks of branched fibrils, very different from the straight fibrils observed for Abeta(1–42) alone. Neurotoxicity assays show that although betaAbetaAKLVFF alters the fibrillization of Abeta(1–42), it does not decrease the neurotoxicity, which suggests that toxic oligomeric Abeta(1–42) species are still present in the betaAbetaAKLVFF/Abeta(1–42) mixtures. Our results show that our designed peptide binds to Abeta(1–42) and changes the amyloid fibril morphology. This is shown to not necessarily translate into reduced toxicity.

Sterically controlled recognition of macromolecular sequence information by molecular tweezers

Sequence-specific binding is demonstrated between pyrene-based tweezer molecules and soluble, high molar mass copolyimides. The binding involves complementary pi - pi stacking interactions, polymer chain-folding, and hydrogen bonding and is extremely sensitive to the steric environment around the pyromellitimide binding-site. A detailed picture of the intermolecular interactions involved has been obtained through single-crystal X-ray studies of tweezer complexes with model diimides. Ring-current magnetic shielding of polyimide protons by the pyrene '' arms '' of the tweezer molecule induces large complexation shifts of the corresponding H-1 NMR resonances, enabling specific triplet sequences to be identified by their complexation shifts. Extended comonomer sequences (triplets of triplets in which the monomer residues differ only by the presence or absence of a methyl group) can be '' read '' by a mechanism which involves multiple binding of tweezer molecules to adjacent diimide residues within the copolymer chain. The adjacent-binding model for sequence recognition has been validated by two conceptually different sets of tweezer binding experiments. One approach compares sequence-recognition events for copolyimides having either restricted or unrestricted triple-triplet sequences, and the other makes use of copolymers containing both strongly binding and completely nonbinding diimide residues. In all cases the nature and relative proportions of triple-triplet sequences predicted by the adjacent-binding model are fully consistent with the observed H-1 NMR data.

Differential recognition of Staphylococcus aureus quorum-sensing signals depends on both extracellular loops 1 and 2 of the transmembrane sensor AgrC.

Virulence in Staphylococcus aureus is regulated via agr-dependent quorum sensing in which an autoinducing peptide (AIP) activates AgrC, a histidine protein kinase. AIPs are usually thiolactones containing seven to nine amino acid residues in which the thiol of the central cysteine is linked to the alpha-carboxyl of the C-terminal amino acid residue. The staphylococcal agr locus has diverged such that the AIPs of the four different S. aureus agr groups self-activate but cross-inhibit. Consequently, although the agr system is conserved among the staphylococci, it has undergone significant evolutionary divergence whereby to retain functionality, any changes in the AIP-encoding gene (agrD) that modifies AIP structure must be accompanied by corresponding changes in the AgrC receptor. Since AIP-1 and AIP-4 only differ by a single amino acid, we compared the transmembrane topology of AgrC1 and AgrC4 to identify amino acid residues involved in AIP recognition. As only two of the three predicted extracellular loops exhibited amino acid differences, site-specific mutagenesis was used to exchange the key AgrC1 and AgrC4 amino acid residues in each loop either singly or in combination. A novel lux-based agrP3 reporter gene fusion was constructed to evaluate the response of the mutated AgrC receptors. The data obtained revealed that while differential recognition of AIP-1 and AIP-4 depends primarily on three amino acid residues in loop 2, loop 1 is essential for receptor activation by the cognate AIP. Furthermore, a single mutation in the AgrC1 loop 2 resulted in conversion of (Ala5)AIP-1 from a potent antagonist to an activator, essentially resulting in the forced evolution of a new AIP group. Taken together, our data indicate that loop 2 constitutes the predicted hydrophobic pocket that binds the AIP thiolactone ring while the exocyclic amino acid tail interacts with loop 1 to facilitate receptor activation.

A novel shape feature for fast region-based pedestrian recognition

A new class of shape features for region classification and high-level recognition is introduced. The novel Randomised Region Ray (RRR) features can be used to train binary decision trees for object category classification using an abstract representation of the scene. In particular we address the problem of human detection using an over segmented input image. We therefore do not rely on pixel values for training, instead we design and train specialised classifiers on the sparse set of semantic regions which compose the image. Thanks to the abstract nature of the input, the trained classifier has the potential to be fast and applicable to extreme imagery conditions. We demonstrate and evaluate its performance in people detection using a pedestrian dataset.

Using objects to coordinate design activity in interaction

The coordination of design is a multi-faceted problem in construction. In design interactions in particular the real-time coordination of design activity is a persistent concern. The use of objects to coordinate the activity of design is studied as this happens in interactions between an architect and a building user group, in a setting where maintaining awareness of the design situation is important. An account of ways in which this was accomplished and how design activity is coordinated through interactional practices is provided. The empirical analyses examine design interaction from an ethnomethodological/conversation analysis (EM/CA) informed perspective to examine: ways in which mutual orientation to design issues are accomplished, how objects can provide a resource for the recognition of the activities of others and ways in which objects might be observable as momentarily intelligible. Subtle interactional practices involving talk, gesture and gaze were some of the small ways in which mutual orientation to the design actions of others became observable. The production of actions sequentially, in response to another's action, marked the real-time coordination of design moves in this setting. The relevance of accounts of micro-interaction to develop understanding of design activity and how it is coordinated are considered.