954 resultados para decentralised data fusion


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Several track-before-detection approaches for image based aircraft detection have recently been examined in an important automated aircraft collision detection application. A particularly popular approach is a two stage processing paradigm which involves: a morphological spatial filter stage (which aims to emphasize the visual characteristics of targets) followed by a temporal or track filter stage (which aims to emphasize the temporal characteristics of targets). In this paper, we proposed new spot detection techniques for this two stage processing paradigm that fuse together raw and morphological images or fuse together various different morphological images (we call these approaches morphological reinforcement). On the basis of flight test data, the proposed morphological reinforcement operations are shown to offer superior signal to-noise characteristics when compared to standard spatial filter options (such as the close-minus-open and adaptive contour morphological operations). However, system operation characterised curves, which examine detection verses false alarm characteristics after both processing stages, illustrate that system performance is very data dependent.

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Study Design. Analysis of a case series of 24 Lenke 1C adolescent idiopathic scoliosis (AIS) patients receiving selective thoracoscopic anterior scoliosis correction. Objective. To report the behaviour of the compensatory lumbar curve in a group of Lenke IC AIS patients following thoracoscopic anterior scoliosis correction, and to compare the results of this study with previously published data. Summary of Background Data. Several prior studies have reported spontaneous lumbar curve correction for both anterior and posterior selective fusion in Lenke 1C/King-Moe II patients; however to our knowledge no previous studies have reported outcomes of thoracoscopic anterior correction for this curve type. Methods. All AIS patients with a curve classification of Lenke 1C and a minimum of 24 months follow-up were retrieved from a consecutive series of 190 AIS patients who underwent thoracoscopic anterior instrumented fusion. Cobb angles of the major curve, instrumented levels, compensatory lumbar curve, and T5-T12 kyphosis were recorded, as well as coronal spinal balance, T1 tilt angle and shoulder balance. All radiographic parameters were measured before surgery and at 2, 6, 12 and 24 months after surgery. Results. Twenty-four female patients with right thoracic curves had a mean thoracic Cobb angle of 53.0° before surgery, decreasing to 24.9° two years after surgery. The mean lumbar compensatory Cobb angle was 43.5° before surgery, spontaneously correcting to 25.4° two years after surgery, indicating balance between the thoracic and lumbar scoliotic curves. The lumbar correction achieved (41.8%) compares favourably to previous studies. Conclusions. Selective thoracoscopic anterior fusion allows spontaneous lumbar curve correction and achieves coronal balance of main thoracic and compensatory lumbar curves, good cosmesis and patient satisfaction. Correction and balance are maintained 24 months after surgery.

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Background Cancer outlier profile analysis (COPA) has proven to be an effective approach to analyzing cancer expression data, leading to the discovery of the TMPRSS2 and ETS family gene fusion events in prostate cancer. However, the original COPA algorithm did not identify down-regulated outliers, and the currently available R package implementing the method is similarly restricted to the analysis of over-expressed outliers. Here we present a modified outlier detection method, mCOPA, which contains refinements to the outlier-detection algorithm, identifies both over- and under-expressed outliers, is freely available, and can be applied to any expression dataset. Results We compare our method to other feature-selection approaches, and demonstrate that mCOPA frequently selects more-informative features than do differential expression or variance-based feature selection approaches, and is able to recover observed clinical subtypes more consistently. We demonstrate the application of mCOPA to prostate cancer expression data, and explore the use of outliers in clustering, pathway analysis, and the identification of tumour suppressors. We analyse the under-expressed outliers to identify known and novel prostate cancer tumour suppressor genes, validating these against data in Oncomine and the Cancer Gene Index. We also demonstrate how a combination of outlier analysis and pathway analysis can identify molecular mechanisms disrupted in individual tumours. Conclusions We demonstrate that mCOPA offers advantages, compared to differential expression or variance, in selecting outlier features, and that the features so selected are better able to assign samples to clinically annotated subtypes. Further, we show that the biology explored by outlier analysis differs from that uncovered in differential expression or variance analysis. mCOPA is an important new tool for the exploration of cancer datasets and the discovery of new cancer subtypes, and can be combined with pathway and functional analysis approaches to discover mechanisms underpinning heterogeneity in cancers

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Mobile devices and smartphones have become a significant communication channel for everyday life. The sensing capabilities of mobile devices are expanding rapidly, and sensors embedded in these devices are cheaper and more powerful than before. It is evident that mobile devices have become the most suitable candidates to sense contextual information without needing extra tools. However, current research shows only a limited number of sensors are being explored and investigated. As a result, it still needs to be clarified what forms of contextual information extracted from mo- bile sensors are useful. Therefore, this research investigates the context sensing using current mobile sensors, the study follows experimental methods and sensor data is evaluated and synthesised, in order to deduce the value of various sensors and combinations of sensor for the use in context-aware mobile applications. This study aims to develop a context fusion framework that will enhance the context-awareness on mobile applications, as well as exploring innovative techniques for context sensing on smartphone devices.

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Reliability of the performance of biometric identity verification systems remains a significant challenge. Individual biometric samples of the same person (identity class) are not identical at each presentation and performance degradation arises from intra-class variability and inter-class similarity. These limitations lead to false accepts and false rejects that are dependent. It is therefore difficult to reduce the rate of one type of error without increasing the other. The focus of this dissertation is to investigate a method based on classifier fusion techniques to better control the trade-off between the verification errors using text-dependent speaker verification as the test platform. A sequential classifier fusion architecture that integrates multi-instance and multisample fusion schemes is proposed. This fusion method enables a controlled trade-off between false alarms and false rejects. For statistically independent classifier decisions, analytical expressions for each type of verification error are derived using base classifier performances. As this assumption may not be always valid, these expressions are modified to incorporate the correlation between statistically dependent decisions from clients and impostors. The architecture is empirically evaluated by applying the proposed architecture for text dependent speaker verification using the Hidden Markov Model based digit dependent speaker models in each stage with multiple attempts for each digit utterance. The trade-off between the verification errors is controlled using the parameters, number of decision stages (instances) and the number of attempts at each decision stage (samples), fine-tuned on evaluation/tune set. The statistical validation of the derived expressions for error estimates is evaluated on test data. The performance of the sequential method is further demonstrated to depend on the order of the combination of digits (instances) and the nature of repetitive attempts (samples). The false rejection and false acceptance rates for proposed fusion are estimated using the base classifier performances, the variance in correlation between classifier decisions and the sequence of classifiers with favourable dependence selected using the 'Sequential Error Ratio' criteria. The error rates are better estimated by incorporating user-dependent (such as speaker-dependent thresholds and speaker-specific digit combinations) and class-dependent (such as clientimpostor dependent favourable combinations and class-error based threshold estimation) information. The proposed architecture is desirable in most of the speaker verification applications such as remote authentication, telephone and internet shopping applications. The tuning of parameters - the number of instances and samples - serve both the security and user convenience requirements of speaker-specific verification. The architecture investigated here is applicable to verification using other biometric modalities such as handwriting, fingerprints and key strokes.

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Facial expression recognition (FER) systems must ultimately work on real data in uncontrolled environments although most research studies have been conducted on lab-based data with posed or evoked facial expressions obtained in pre-set laboratory environments. It is very difficult to obtain data in real-world situations because privacy laws prevent unauthorized capture and use of video from events such as funerals, birthday parties, marriages etc. It is a challenge to acquire such data on a scale large enough for benchmarking algorithms. Although video obtained from TV or movies or postings on the World Wide Web may also contain ‘acted’ emotions and facial expressions, they may be more ‘realistic’ than lab-based data currently used by most researchers. Or is it? One way of testing this is to compare feature distributions and FER performance. This paper describes a database that has been collected from television broadcasts and the World Wide Web containing a range of environmental and facial variations expected in real conditions and uses it to answer this question. A fully automatic system that uses a fusion based approach for FER on such data is introduced for performance evaluation. Performance improvements arising from the fusion of point-based texture and geometry features, and the robustness to image scale variations are experimentally evaluated on this image and video dataset. Differences in FER performance between lab-based and realistic data, between different feature sets, and between different train-test data splits are investigated.

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This document describes large, accurately calibrated and time-synchronised datasets, gathered in controlled environmental conditions, using an unmanned ground vehicle equipped with a wide variety of sensors. These sensors include: multiple laser scanners, a millimetre wave radar scanner, a colour camera and an infra-red camera. Full details of the sensors are given, as well as the calibration parameters needed to locate them with respect to each other and to the platform. This report also specifies the format and content of the data, and the conditions in which the data have been gathered. The data collection was made in two different situations of the vehicle: static and dynamic. The static tests consisted of sensing a fixed ’reference’ terrain, containing simple known objects, from a motionless vehicle. For the dynamic tests, data were acquired from a moving vehicle in various environments, mainly rural, including an open area, a semi-urban zone and a natural area with different types of vegetation. For both categories, data have been gathered in controlled environmental conditions, which included the presence of dust, smoke and rain. Most of the environments involved were static, except for a few specific datasets which involve the presence of a walking pedestrian. Finally, this document presents illustrations of the effects of adverse environmental conditions on sensor data, as a first step towards reliability and integrity in autonomous perceptual systems.

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In this paper we present large, accurately calibrated and time-synchronized data sets, gathered outdoors in controlled and variable environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. These include four 2D laser scanners, a radar scanner, a color camera and an infrared camera. It provides a full description of the system used for data collection and the types of environments and conditions in which these data sets have been gathered, which include the presence of airborne dust, smoke and rain.

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Introduction Well-designed biodegradable scaffolds in combination with bone growth factors offer a valuable alternative to the current gold standard autograft in spinal fusion surgery Yong et al. (2013). Here we report on 6- vs 12- month data set evaluating the longitudinal performance of a CaP coated polycaprolactone (PCL) scaffold loaded with recombinant human bone morphogenetic protein-2 (rhBMP-2) as a bone graft substitute within a large preclinical animal model. Methods Twelve sheep underwent a 3-level (T6/7, T8/9 and T10/11) discectomy with randomly allocated implantation of a different graft substitute at each of the three levels; (i) calcium phosphate (CaP) coated polycaprolactone based scaffold plus 0.54µg rhBMP-2, (ii) CaP coated PCL- based scaffold alone or (iii) autograft (mulched rib head). Fusion assessments were performed via high resolution clinical computed tomography and histological evaluation were undertaken at six (n=6) and twelve (n=6) months post-surgery using the Sucato grading system (Sucato et al. 2004). Results The computed tomography fusion grades of the 6- and 12- months in the rhBMP-2 plus PCL- based scaffold group were 1.9 and 2.1 respectively, in the autograft group 1.9 and 1.3 respectively, and in the scaffold alone group 0.9 and 1.17 respectively. There were no statistically significant differences in the fusion scores between 6- and 12- month for the rhBMP plus PCL- based scaffold or PCL – based scaffold alone group however there was a significant reduction in scores in the autograft group. These scores were seen to correlate with histological evaluations of the respective groups. Conclusions The results of this study demonstrate the efficacy of scaffold-based delivery of rhBMP-2 in promoting higher fusion grades at 6- and 12- months in comparison to the scaffold alone or autograft group within the same time frame. Fusion grades achieved at six months using PCL+rhBMP-2 are not significantly increased at twelve months post-surgery.

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While existing multi-biometic Dempster-Shafer the- ory fusion approaches have demonstrated promising perfor- mance, they do not model the uncertainty appropriately, sug- gesting that further improvement can be achieved. This research seeks to develop a unified framework for multimodal biometric fusion to take advantage of the uncertainty concept of Dempster- Shafer theory, improving the performance of multi-biometric authentication systems. Modeling uncertainty as a function of uncertainty factors affecting the recognition performance of the biometric systems helps to address the uncertainty of the data and the confidence of the fusion outcome. A weighted combination of quality measures and classifiers performance (Equal Error Rate) are proposed to encode the uncertainty concept to improve the fusion. We also found that quality measures contribute unequally to the recognition performance, thus selecting only significant factors and fusing them with a Dempster-Shafer approach to generate an overall quality score play an important role in the success of uncertainty modeling. The proposed approach achieved a competitive performance (approximate 1% EER) in comparison with other Dempster-Shafer based approaches and other conventional fusion approaches.

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Multidimensional data are getting increasing attention from researchers for creating better recommender systems in recent years. Additional metadata provides algorithms with more details for better understanding the interaction between users and items. While neighbourhood-based Collaborative Filtering (CF) approaches and latent factor models tackle this task in various ways effectively, they only utilize different partial structures of data. In this paper, we seek to delve into different types of relations in data and to understand the interaction between users and items more holistically. We propose a generic multidimensional CF fusion approach for top-N item recommendations. The proposed approach is capable of incorporating not only localized relations of user-user and item-item but also latent interaction between all dimensions of the data. Experimental results show significant improvements by the proposed approach in terms of recommendation accuracy.

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Available industrial energy meters offer high accuracy and reliability, but are typically expensive and low-bandwidth, making them poorly suited to multi-sensor data acquisition schemes and power quality analysis. An alternative measurement system is proposed in this paper that is highly modular, extensible and compact. To minimise cost, the device makes use of planar coreless PCB transformers to provide galvanic isolation for both power and data. Samples from multiple acquisition devices may be concentrated by a central processor before integration with existing host control systems. This paper focusses on the practical design and implementation of planar coreless PCB transformers to facilitate the module's isolated power, clock and data signal transfer. Calculations necessary to design coreless PCB transformers, and circuits designed for the transformer's practical application in the measurement module are presented. The designed transformer and each application circuit have been experimentally verified, with test data and conclusions made applicable to coreless PCB transformers in general.

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Introduction. Rett Syndrome is a rare genetic neurodevelopmental disorder usually affecting females. Scoliosis is a common comorbidity and spinal fusion may be recommended if severe. Little is known about long term outcomes. We examined the impact of spinal fusion on survival and risk of severe lower respiratory tract infection (LRTI) in Rett Syndrome. Methods Data were ascertained from hospital medical records, the Australian Rett Syndrome Database, a longitudinal and population-based registry of Rett Syndrome cases established in 1993, and the Australian Institute of Health and Welfare National Death Index database. An extended Cox regression model was used to estimate the effect of spinal surgery on survival in females who developed severe scoliosis (Cobb angle > 45 degrees). Generalized estimating equation modelling was used to estimate the effect of spinal surgery on the odds of developing severe LRTI. Results Severe scoliosis was identified in 140 cases (60.3%) of whom slightly fewer than half (48.6%) developed scoliosis prior to eight years of age. Scoliosis surgery was performed in 98 (69.0%) of those at a median age of 13 years 3 months (IQR 11 years 5 months – 14 years 10 months). After adjusting for mutation type and age of scoliosis onset, the rate of death was lower in the surgery group (HR 0.30, 95% CI 0.12, 0.74, P = 0.009) compared to those without surgery. Rate of death was particularly reduced for those with early onset scoliosis (HR 0.17, 95% CI 0.06, 0.52, P = 0.002). Spinal fusion was not associated with reduction in the occurrence of a severe LRTI overall (OR 0.60, 95%CI 0.27, 1.33, P=0.206) but was associated with a large reduction in odds of severe LRTI among those with early onset scoliosis (OR 0.32, 95%CI 0.11, 0.93, P=0.036). Conclusion With appropriate cautions, spinal fusion confers an advantage to life expectancy in Rett syndrome.

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A scheme for integration of stand-alone INS and GPS sensors is presented, with data interchange over an external bus. This ensures modularity and sensor interchangeability. Use of a medium-coupled scheme reduces data flow and computation, facilitating use in surface vehicles. Results show that the hybrid navigation system is capable of delivering high positioning accuracy.

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Study Design Retrospective review of prospectively collected data. Objectives To analyze intervertebral (IV) fusion after thoracoscopic anterior spinal fusion (TASF) and explore the relationship between fusion scores and key clinical variables. Summary of Background Information TASF provides comparable correction with some advantages over posterior approaches but reported mechanical complications, and their relationship to non-union and graft material is unclear. Similarly, the optimal combination of graft type and implant stiffness for effecting successful radiologic union remains undetermined. Methods A subset of patients from a large single-center series who had TASF for progressive scoliosis underwent low-dose computed tomographic scans 2 years after surgery. The IV fusion mass in the disc space was assessed using the 4-point Sucato scale, where 1 indicates <50% and 4 indicates 100% bony fusion of the disc space. The effects of rod diameter, rod material, graft type, fusion level, and mechanical complications on fusion scores were assessed. Results Forty-three patients with right thoracic major curves (mean age 14.9 years) participated in the study. Mean fusion scores for patient subgroups ranged from 1.0 (IV levels with rod fractures) to 2.2 (4.5-mm rod with allograft), with scores tending to decrease with increasing rod size and stiffness. Graft type (autograft vs. allograft) did not affect fusion scores. Fusion scores were highest in the middle levels of the rod construct (mean 2.52), dropping off by 20% to 30% toward the upper and lower extremities of the rod. IV levels where a rod fractured had lower overall mean fusion scores compared to levels without a fracture. Mean total Scoliosis Research Society (SRS) questionnaire scores were 98.9 from a possible total of 120, indicating a good level of patient satisfaction. Conclusions Results suggest that 100% radiologic fusion of the entire disc space is not necessary for successful clinical outcomes following thoracoscopic anterior selective thoracic fusion.