Fast recognition of musical sounds based on timbre

Human listeners seem to have an impressive ability to recognize a wide variety of natural sounds. However, there is surprisingly little quantitative evidence to characterize this fundamental ability. Here the speed and accuracy of musical-sound recognition were measured psychophysically with a rich but acoustically balanced stimulus set. The set comprised recordings of notes from musical instruments and sung vowels. In a first experiment, reaction times were collected for three target categories: voice, percussion, and strings. In a go/no-go task, listeners reacted as quickly as possible to members of a target category while withholding responses to distractors (a diverse set of musical instruments). Results showed near-perfect accuracy and fast reaction times, particularly for voices. In a second experiment, voices were recognized among strings and vice-versa. Again, reaction times to voices were faster. In a third experiment, auditory chimeras were created to retain only spectral or temporal features of the voice. Chimeras were recognized accurately, but not as quickly as natural voices. Altogether, the data suggest rapid and accurate neural mechanisms for musical-sound recognition based on selectivity to complex spectro-temporal signatures of sound sources.

Electrochemical sensor for catechol and dopamine based on a catalytic molecularly imprinted polymer-conducting polymer hybrid recognition element

One of the difficulties with using molecularly imprinted polymers (MIPs) and other electrically insulating materials as the recognition element in electrochemical sensors is the lack of a direct path for the conduction of electrons from the active sites to the electrode. We have sought to address this problem through the preparation and characterization of novel hybrid materials combining a catalytic MIP, capable of oxidizing the template, catechol, with an electrically conducting polymer. In this way a network of "molecular wires" assists in the conduction of electrons from the active sites within the MIP to the electrode surface. This was made possible by the design of a new monomer that combines orthogonal polymerizable functionality; comprising an aniline group and a methacrylamide. Conducting films were prepared on the surface of electrodes (Au on glass) by electropolymerization of the aniline moiety. A layer of MIP was photochemically grafted over the polyaniline, via N,N'-diethyldithiocarbamic acid benzyl ester (iniferter) activation of the methacrylamide groups. Detection of catechol by the hybrid-MIP sensor was found to be specific, and catechol oxidation was detected by cyclic voltammetry at the optimized operating conditions: potential range -0.6 V to +0.8 V (vs Ag/AgCl), scan rate 50 mV/s, PBS pH 7.4. The calibration curve for catechol was found to be linear to 144 µM, with a limit of detection of 228 nM. Catechol and dopamine were detected by the sensor, whereas analogues and potentially interfering compounds, including phenol, resorcinol, hydroquinone, serotonin, and ascorbic acid, had minimal effect (=3%) on the detection of either analyte. Nonimprinted hybrid electrodes and bare gold electrodes failed to give any response to catechol at concentrations below 0.5 mM. Finally, the catalytic properties of the sensor were characterized by chronoamperometry and were found to be consistent with Michaelis-Menten kinetics. © 2009 American Chemical Society.

Experimental investigation of a wavelet based drive-by bridge inspection system incorporating pattern recognition

This paper presents the results of an experimental investigation, carried out in order to verify the feasibility of a ‘drive-by’ approach which uses a vehicle instrumented with accelerometers to detect and locate damage in a bridge. In theoretical simulations, a simplified vehicle-bridge interaction model is used to investigate the effectiveness of the approach in detecting damage in a bridge from vehicle accelerations. For this purpose, the accelerations are processed using a continuous wavelet transform and damage indicators are evaluated and compared. Alternative statistical pattern recognition techniques are incorporated to allow for repeated vehicle passes. Parameters such as vehicle speed, damage level, location and road roughness are varied in simulations to investigate the effect. A scaled laboratory experiment is carried out to assess the effectiveness of the approach in a more realistic environment, considering a number of bridge damage scenarios.

Robust 2D Ear Registration and Recognition Based on SIFT Point Matching

Significant recent progress has shown ear recognition to be a viable biometric. Good recognition rates have been demonstrated under controlled conditions, using manual registration or with specialised equipment. This paper describes a new technique which improves the robustness of ear registration and recognition, addressing issues of pose variation, background clutter and occlusion. By treating the ear as a planar surface and creating a homography transform using SIFT feature matches, ears can be registered accurately. The feature matches reduce the gallery size and enable a precise ranking using a simple 2D distance algorithm. When applied to the XM2VTS database it gives results comparable to PCA with manual registration. Further analysis on more challenging datasets demonstrates the technique to be robust to background clutter, viewing angles up to +/- 13 degrees and with over 20% occlusion.

Exploring molecular recognition pathways in one- and two-component gels formed by dendritic lysine-based gelators

This paper provides an integrated overview of the factors which control gelation in a family of dendritic gelators based on lysine building blocks. In particular, we establish that higher generation systems are more effective gelators, amide linkages in the dendron are better than carbamates, and long alkyl chain surface groups and a carboxylic acid at the focal point enhance gelation. The gels are best formed in relatively low polarity solvents with no hydrogen bond donor ability and limited hydrogen bond acceptor capacity. The dendrons with acid groups at the focal point can form two component gels with diaminododecane, and in this case, it is the lower generation dendrons which can avoid steric hindrance and form more effective gels. The stereochemistry of lysine is crucial in self-assembly, with opposite enantiomers disrupting each other's molecular recognition pathways. For the two-component system, stoichiometry is key, if too much diamine is present, dendron-stabilised microcrystals of the diamine begin to form. Interestingly, gelation still occurs in this case, and the systems with amides/alkyl chains are more effective gels, as a consequence of enhanced dendron-dendron intermolecular interactions allowing the microcrystals to form an interconnected network.

An indirect bridge inspection method incorporating a wavelet-based damage indicator and pattern recognition

In recent years, there has been a move towards the development of indirect structural health monitoring (SHM)techniques for bridges; the low-cost vibration-based method presented in this paper is such an approach. It consists of the use of a moving vehicle fitted with accelerometers on its axles and incorporates wavelet analysis and statistical pattern recognition. The aim of the approach is to both detect and locate damage in bridges while reducing the need for direct instrumentation of the bridge. In theoretical simulations, a simplified vehicle-bridge interaction model is used to investigate the effectiveness of the approach in detecting damage in a bridge from vehicle accelerations. For this purpose, the accelerations are processed using a continuous wavelet transform as when the axle passes over a damaged section, any discontinuity in the signal would affect the wavelet coefficients. Based on these coefficients, a damage indicator is formulated which can distinguish between different damage levels. However, it is found to be difficult to quantify damage of varying levels when the vehicle’s transverse position is varied between bridge crossings. In a real bridge field experiment, damage was applied artificially to a steel truss bridge to test the effectiveness of the indirect approach in practice; for this purpose a two-axle van was driven across the bridge at constant speed. Both bridge and vehicle acceleration measurements were recorded. The dynamic properties of the test vehicle were identified initially via free vibration tests. It was found that the resulting damage indicators for the bridge and vehicle showed similar patterns, however, it was difficult to distinguish between different artificial damage scenarios.

Pattern Recognition Approach for Road Collision Hotspots Analysis: Case Study of Northern Ireland

In order to address road safety effectively, it is essential to understand all the factors, which
attribute to the occurrence of a road collision. This is achieved through road safety
assessment measures, which are primarily based on historical crash data. Recent advances
in uncertain reasoning technology have led to the development of robust machine learning
techniques, which are suitable for investigating road traffic collision data. These techniques
include supervised learning (e.g. SVM) and unsupervised learning (e.g. Cluster Analysis).
This study extends upon previous research work, carried out in Coll et al. [3], which
proposed a non-linear aggregation framework for identifying temporal and spatial hotspots.
The results from Coll et al. [3] identified Lisburn area as the hotspot, in terms of road safety,
in Northern Ireland. This study aims to use Cluster Analysis, to investigate and highlight any
hidden patterns associated with collisions that occurred in Lisburn area, which in turn, will
provide more clarity in the causation factors so that appropriate countermeasures can be put
in place.

Video Event Recognition by Dempster-Shafer Theory

This paper presents an event recognition framework, based on Dempster-Shafer theory, that combines evidence of events from low-level computer vision analytics. The proposed method employing evidential network modelling of composite events, is able to represent uncertainty of event output from low level video analysis and infer high level events with semantic meaning along with degrees of belief. The method has been evaluated on videos taken of subjects entering and leaving a seated area. This has relevance to a number of transport scenarios, such as onboard buses and trains, and also in train stations and airports. Recognition results of 78% and 100% for four composite events are encouraging.

Region-based Mixture of Gaussians Modelling for Foreground Detection in Dynamic Scenes

One of the most widely used techniques in computer vision for foreground detection is to model each background pixel as a Mixture of Gaussians (MoG). While this is effective for a static camera with a fixed or a slowly varying background, it fails to handle any fast, dynamic movement in the background. In this paper, we propose a generalised framework, called region-based MoG (RMoG), that takes into consideration neighbouring pixels while generating the model of the observed scene. The model equations are derived from Expectation Maximisation theory for batch mode, and stochastic approximation is used for online mode updates. We evaluate our region-based approach against ten sequences containing dynamic backgrounds, and show that the region-based approach provides a performance improvement over the traditional single pixel MoG. For feature and region sizes that are equal, the effect of increasing the learning rate is to reduce both true and false positives. Comparison with four state-of-the art approaches shows that RMoG outperforms the others in reducing false positives whilst still maintaining reasonable foreground definition. Lastly, using the ChangeDetection (CDNet 2014) benchmark, we evaluated RMoG against numerous surveillance scenes and found it to amongst the leading performers for dynamic background scenes, whilst providing comparable performance for other commonly occurring surveillance scenes.

Predictors of place of death for those in receipt of home-based palliative care services in Ontario, Canada

Many cancer patients die in institutional settings despite their preference to die at home. A longitudinal, prospective cohort study was conducted to comprehensively assess the determinants of home death for patients receiving home-based palliative care. Data collected from biweekly telephone interviews with caregivers (n=302) and program databases were entered into a multivariate logistic model. Patients with high nursing costs (odds ratio [OR]: 4.3; confidence interval [CI]: 1.8-10.2) and patients with high personal support worker costs (OR: 2.3; CI: 1.1-4.5) were more likely to die at home than those with low costs. Patients who lived alone were less likely to die at home than those who cohabitated (OR: 0.4; CI: 0.2-0.8), and those with a high propensity for a home-death preference were more likely to die at home than those with a low propensity (OR: 5.8; CI: 1.1-31.3). An understanding of the predictors of place of death may contribute to the development of effective interventions that support home death.

A realistic evaluation of track and trigger systems and acute care training for early recognition and treatment of deteriorating ward-based patients: research protocol.

Aim The aim of the study is to evaluate factors that enable or constrain the implementation and service delivery of early warnings systems or acute care training in practice. Background To date there is limited evidence to support the effectiveness of acute care initiatives (early warning systems, acute care training, outreach) in reducing the number of adverse events (cardiac arrest, death, unanticipated Intensive Care admission) through increased recognition and management of deteriorating ward based patients in hospital [1-3]. The reasons posited are that previous research primarily focused on measuring patient outcomes following the implementation of an intervention or programme without considering the social factors (the organisation, the people, external influences) which may have affected the process of implementation and hence measured end-points. Further research which considers the social processes is required in order to understand why a programme works, or does not work, in particular circumstances [4]. Method The design is a multiple case study approach of four general wards in two acute hospitals where Early Warning Systems (EWS) and Acute Life-threatening Events Recognition and Treatment (ALERT) course have been implemented. Various methods are being used to collect data about individual capacities, interpersonal relationships and institutional balance and infrastructures in order to understand the intended and unintended process outcomes of implementing EWS and ALERT in practice. This information will be gathered from individual and focus group interviews with key participants (ALERT facilitators, nursing and medical ALERT instructors, ward managers, doctors, ward nurses and health care assistants from each hospital); non-participant observation of ward organisation and structure; audit of patients' EWS charts and audit of the medical notes of patients who deteriorated during the study period to ascertain whether ALERT principles were followed. Discussion & progress to date This study commenced in January 2007. Ethical approval has been granted and data collection is ongoing with interviews being conducted with key stakeholders. The findings from this study will provide evidence for policy-makers to make informed decisions regarding the direction for strategic and service planning of acute care services to improve the level of care provided to acutely ill patients in hospital. References 1. Esmonde L, McDonnell A, Ball C, Waskett C, Morgan R, Rashidain A et al. Investigating the effectiveness of Critical Care Outreach Services: A systematic review. Intensive Care Medicine 2006; 32: 1713-1721 2. McGaughey J, Alderdice F, Fowler R, Kapila A, Mayhew A, Moutray M. Outreach and Early Warning Systems for the prevention of Intensive Care admission and death of critically ill patients on general hospital wards. Cochrane Database of Systematic Reviews 2007, Issue 3. www.thecochranelibrary.com 3. Winters BD, Pham JC, Hunt EA, Guallar E, Berenholtz S, Pronovost PJ (2007) Rapid Response Systems: A systematic review. Critical Care Medicine 2007; 35 (5): 1238-43 4. Pawson R and Tilley N. Realistic Evaluation. London; Sage: 1997

The Effect of Community-Based Specialist Palliative Care Teams on Place of Care

BACKGROUND: Prior research on community-based specialist palliative care teams used outcome measures of place of death and/or dichotomous outcome measures of acute care use in the last two weeks of life. However, existing research seldom measured the diverse places of care used and their timing prior to death.

OBJECTIVE: The study objective was to examine the place of care in the last 30 days of life.

METHODS: In this retrospective cohort study, patients who received care from a specialist palliative care team (exposed) were matched by propensity score to patients who received usual care in the community (unexposed) in Ontario, Canada. Measured was the percentage of patients in each place of care in the last month of life as a proportion of the total cohort.

RESULTS: After matching, 3109 patients were identified in each group, where 79% had cancer and 77% received end-of-life home care. At 30 days compared to 7 days before death, the exposed group's proportions rose from 33% to 41% receiving home care and 14% to 15% in hospital, whereas the unexposed group's proportions rose from 28% to 32% receiving home care and 16% to 22% in hospital. Linear trend analysis (proportion over time) showed that the exposed group used significantly more home care services and fewer hospital days (p < 0.001) than the unexposed group. On the last day of life (place of death), the exposed group had 18% die in an in-patient hospital bed compared to 29% in usual care.

CONCLUSION: Examining place of care in the last month can effectively illustrate the service use trajectory over time.

Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

A frontal view gait recognition based on 3D imaging using a time of flight camera

Studies have been carried out to recognize individuals from a frontal view using their gait patterns. In previous work, gait sequences were captured using either single or stereo RGB camera systems or the Kinect 1.0 camera system. In this research, we used a new frontal view gait recognition method using a laser based Time of Flight (ToF) camera. In addition to the new gait data set, other contributions include enhancement of the silhouette segmentation, gait cycle estimation and gait image representations. We propose four new gait image representations namely Gait Depth Energy Image (GDE), Partial GDE (PGDE), Discrete Cosine Transform GDE (DGDE) and Partial DGDE (PDGDE). The experimental results show that all the proposed gait image representations produce better accuracy than the previous methods. In addition, we have also developed Fusion GDEs (FGDEs) which achieve better overall accuracy and outperform the previous methods.