Vulnerabilities in first-generation RFID-enabled credit cards

Credit cards ; Radio frequency identification systems

Transfer learning for classification of experimental ultrasonic non-destructive testing images from synthetic data

Lack of experimental training data is a significant challenge for the use of Deep Learning algorithms in Non-Destructive Testing. This work provides a Transfer Learning solution to the challenge of low training data volumes in Non-Destructive Ultrasonic Testing of carbon fibre reinforced polymer composites, which are known for their high structural ultrasonic noise. The performance of Convolutional Neural Networks for classification was initially tested on experimental data when trained on simulated data. The results demonstrated that due to inaccurate noise production the simulated data domain was too far from the experimental test data to provide accurate classification. Different synthetic datasets were then generated using a variety of methods and their effect on classification performance was studied. The primary focus of these datasets were different methods of noise generation for more experimentally accurate simulated images. To allow for the direct comparison of the different synthetic data generation methods, a standardized custom Convolutional Neural Network was developed. To make sure that the Neural Network was complex enough for the solution space hyperparameter optimization was performed on the network using a secondary experimental dataset. The hyperparameter optimization was a variant of Regularized Evolution [1] which was adapted for continuous and integer valued hyperparameters. The algorithm was initialized with a Population of 128 configurations generated via a random search. At each iteration of Regularized Evolution, a parent model was selected from a sample of configurations from the population, with the highest F1 score. A new child configuration was generated by mutating one of the parents hyperparameters. This child model was then trained and prepended to the population with the ‘oldest’ model discarded. The best performing model was then used for comparisons of classification accuracy for different synthetic datasets. The best performing synthetic dataset saw an F1 score increase of 0.34 (0.738-0.394) from the simulated dataset

A comparison of methods for generating synthetic training data for domain adaption of deep learning models in ultrasonic non-destructive evaluation

This work provides a solution to the challenge of small amounts of training data in Non-Destructive Ultrasonic Testing for composite components. It was demonstrated that direct simulation alone is ineffective at producing training data that was representative of the experimental domain due to poor noise reconstruction. Therefore, four unique synthetic data generation methods were proposed which use semi-analytical simulated data as a foundation. Each method was evaluated for its performance in the classification of real experimental images when trained on a Convolutional Neural Network which underwent hyperparameter optimization using a genetic algorithm. The first method introduced task specific modifications to CycleGAN, a generative network for image-to-image translation, to learn the mapping from physics-based simulations of defect indications to experimental indications in resulting ultrasound images. The second method was based on combining real experimental defect free images with simulated defect responses. The final two methods fully simulated the noise responses at an image and signal level respectively. The purely simulated data produced a mean classification F1 score of 0.394. However, when trained on the new synthetic datasets, a significant improvement in classification performance on experimental data was realized, with mean classification F1 scores of 0.843, 0.688, 0.629, and 0.738 for the respective approaches

Automated bounding box annotation for NDT ultrasound defect detection

The growing interest in applying Machine Learning (ML) techniques in Non-Destructive Testing (NDT) to assist expert detection and analysis is facing many unique challenges. This research seeks to create an object detection network that would automatically generate bounding boxes around various defects found in Carbon Fibre Reinforced Polymers (CFRPs) through which the quantitative defect size information can be inferred. CFRPs are structurally anisotropic resulting in complex physical interactions between the emitted acoustic waves and the material structure when Ultrasonic Testing (UT) is deployed. Therefore, the structural noise makes the detection of various types of defects, such as porosities, delaminations and inclusions, that are frequently observed in CFRPs [1] even a more challenging task. In order to take a supervised learning approach in the detection of defects, a training dataset must be produced and labelled. Extensive automatic methods for data collection exist, however, in many cases labelling is done manually, which requires extensive use of expert time. Therefore, a method for automatically labelling simple defects could potentially be useful for accelerating the ground truth creation and allowing experts to focus on the detection of more complex defects

3-Dimensional residual neural architecture search for ultrasonic defect detection

This study presents a deep learning methodology using 3-dimensional (3D) convolutional neural networks to detect defects in carbon fiber reinforced polymer composites through volumetric ultrasonic testing data. Acquiring large amounts of ultrasonic training data experimentally is expensive and time-consuming. To address this issue, a synthetic data generation method was extended to incorporate volumetric data. By preserving the complete volumetric data, complex preprocessing is reduced, and the model can utilize spatial and temporal information that is lost during imaging. This enables the model to utilize important features that might be overlooked otherwise. The performance of three architectures were compared. The first architecture is prevalent in the literature for the classification of volumetric datasets. The second demonstrated a hand-designed approach to architecture design, with modifications to the first architecture to address the challenges of this specific task. A key modification was the use of cuboidal kernels to account for the large aspect ratios seen in ultrasonic data. The third architecture was discovered through neural architecture search from a modified 3D Residual Neural Network (ResNet) search space. Additionally, domain-specific augmentation methods were incorporated during training, resulting in significant improvements in model performance, with a mean accuracy improvement of 22.4% on the discovered architecture. The discovered architecture demonstrated the best performance with a mean accuracy increase of 7.9% over the second best model. It was able to consistently detect all defects whilst maintaining a model size smaller than most 2-dimensional (2D) ResNets. Each model had an inference time of less than 0.5 seconds, making them efficient for the interpretation of large amounts of data

Transforming industrial manipulators via kinesthetic guidance for automated inspection of complex geometries

The increased demand for cost-efficient manufacturing and metrology inspection solutions for complex-shaped components in High-Value Manufacturing (HVM) sectors, requires increased production throughput and precision. This drives the integration of automated robotic solutions. However, the current manipulators utilising traditional programming approaches demand specialised robotic programming knowledge and make it challenging to generate complex paths and adapt easily to unique specifications per component, resulting in an inflexible and cumbersome teaching process. Therefore, this body of work proposes a novel software system, to realize kinesthetic guidance for path planning in real-time intervals at 250 Hz utilizing an external off-the-shelf Force Torque (FT) sensor. The proposed work is demonstrated on a 500 mm2 near net shaped Wire + Arc Additive Manufacturing (WAAM) complex component with embedded defects, by teaching the inspection path for defect detection with a standard industrial robotic manipulator in a collaborative fashion and adaptively generating the kinematics resulting for uniform coupling of ultrasound inspection. The utilized method proved superior performance and speed, accelerating the programming time over online and offline approaches by an estimate of 88% to 98%. The proposed work is a unique development, retrofitting current industrial manipulators into collaborative entities, securing human job resources and achieving flexible production

US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.Comment: 102 pages + reference

Whole Genome Sequencing Reveals Local Transmission Patterns of Mycobacterium bovis in Sympatric Cattle and Badger Populations

Whole genome sequencing (WGS) technology holds great promise as a tool for the forensic epidemiology of bacterial pathogens. It is likely to be particularly useful for studying the transmission dynamics of an observed epidemic involving a largely unsampled ‘reservoir' host, as for bovine tuberculosis (bTB) in British and Irish cattle and badgers. BTB is caused by Mycobacterium bovis, a member of the M. tuberculosis complex that also includes the aetiological agent for human TB. In this study, we identified a spatio-temporally linked group of 26 cattle and 4 badgers infected with the same Variable Number Tandem Repeat (VNTR) type of M. bovis. Single-nucleotide polymorphisms (SNPs) between sequences identified differences that were consistent with bacterial lineages being persistent on or near farms for several years, despite multiple clear whole herd tests in the interim. Comparing WGS data to mathematical models showed good correlations between genetic divergence and spatial distance, but poor correspondence to the network of cattle movements or within-herd contacts. Badger isolates showed between zero and four SNP differences from the nearest cattle isolate, providing evidence for recent transmissions between the two hosts. This is the first direct genetic evidence of M. bovis persistence on farms over multiple outbreaks with a continued, ongoing interaction with local badgers. However, despite unprecedented resolution, directionality of transmission cannot be inferred at this stage. Despite the often notoriously long timescales between time of infection and time of sampling for TB, our results suggest that WGS data alone can provide insights into TB epidemiology even where detailed contact data are not available, and that more extensive sampling and analysis will allow for quantification of the extent and direction of transmission between cattle and badgers

Glastir Monitoring & Evaluation Programme. First year annual report

The Welsh Government has commissioned a comprehensive new ecosystem monitoring and evaluation programme to monitor the effects of Glastir, its new land management scheme, and to monitor progress towards a range of international biodiversity and environmental targets. A random sample of 1 km squares stratified by landcover types will be used both to monitor change at a national level in the wider countryside and to provide a backdrop against which intervention measures are assessed using a second sample of 1 km squares located in areas eligible for enhanced payments for advanced interventions. Modelling in the first year has forecast change based on current understanding, whilst a rolling national monitoring programme based on an ecosystem approach will provide an evidence-base for on-going, adaptive development of the scheme by Welsh Government. To our knowledge, this will constitute the largest and most in-depth ecosystem monitoring and evaluation programme of any member state of the European Union

Peri-operative red blood cell transfusion in neonates and infants: NEonate and Children audiT of Anaesthesia pRactice IN Europe: A prospective European multicentre observational study

BACKGROUND: Little is known about current clinical practice concerning peri-operative red blood cell transfusion in neonates and small infants. Guidelines suggest transfusions based on haemoglobin thresholds ranging from 8.5 to 12 g dl-1, distinguishing between children from birth to day 7 (week 1), from day 8 to day 14 (week 2) or from day 15 (≥week 3) onwards. OBJECTIVE: To observe peri-operative red blood cell transfusion practice according to guidelines in relation to patient outcome. DESIGN: A multicentre observational study. SETTING: The NEonate-Children sTudy of Anaesthesia pRactice IN Europe (NECTARINE) trial recruited patients up to 60 weeks' postmenstrual age undergoing anaesthesia for surgical or diagnostic procedures from 165 centres in 31 European countries between March 2016 and January 2017. PATIENTS: The data included 5609 patients undergoing 6542 procedures. Inclusion criteria was a peri-operative red blood cell transfusion. MAIN OUTCOME MEASURES: The primary endpoint was the haemoglobin level triggering a transfusion for neonates in week 1, week 2 and week 3. Secondary endpoints were transfusion volumes, 'delta haemoglobin' (preprocedure - transfusion-triggering) and 30-day and 90-day morbidity and mortality. RESULTS: Peri-operative red blood cell transfusions were recorded during 447 procedures (6.9%). The median haemoglobin levels triggering a transfusion were 9.6 [IQR 8.7 to 10.9] g dl-1 for neonates in week 1, 9.6 [7.7 to 10.4] g dl-1 in week 2 and 8.0 [7.3 to 9.0] g dl-1 in week 3. The median transfusion volume was 17.1 [11.1 to 26.4] ml kg-1 with a median delta haemoglobin of 1.8 [0.0 to 3.6] g dl-1. Thirty-day morbidity was 47.8% with an overall mortality of 11.3%. CONCLUSIONS: Results indicate lower transfusion-triggering haemoglobin thresholds in clinical practice than suggested by current guidelines. The high morbidity and mortality of this NECTARINE sub-cohort calls for investigative action and evidence-based guidelines addressing peri-operative red blood cell transfusions strategies. TRIAL REGISTRATION: ClinicalTrials.gov, identifier: NCT02350348