Pixel classification for automated diabetic foot diagnosis

Worldwide, more than 180 million people suffer from diabetes mellitus. Approximately 50% of these patients will develop complications to their feet. Neuropathy, combined with poor blood supply and biomechanical changes results in a high risk for foot ulcers, which is a key problem in the diabetic foot; when these wounds become infected, this can ultimately result in lower extremity amputation, which has a serious effect on the quality of life of the patient, and causes a large economic burden on society.\ud \ud This was the motivation for a collaborate project (Vincent50) in which a photographic foot imaging device was developed. The system allows scanning of the foot soles on a daily basis which may lead to early recognition of foot problems. The goal of the present study is to determine whether pixel classification is a useful intermediate step towards automatically assessing the images of the foot soles for signs of diabetic foot disease. If successful, this approach will further relief health care professionals in assessing the foot and enable the placement of more devices in the future. \ud \ud The best agreement between automated recognition and expert diagnosis was achieved with a combination of RGB and derived features, proves that the RGB data is informative with respect to detection of ulcers. However, the automatic detection of pre-signs of ulcers and other anomalies needs more sophistication than pixel classification alone. Firstly, other physical features, such as hyperspectral data, infrared and/or textural features are expected to be more informative. Secondly, we expect to be able to boost the performance by using the context between neighboring pixels. Thirdly, an individualized and normalized classification process might help with the large variability in foot soles between individuals. \u

Molding the symbiosis between human and machine : contributions to anomaly detection, model evaluation, and active learning

The enormous amounts of data make it progressively difficult for a human to assign meaning to individual data points. For example, if several users are active on different devices in a computer network, all kinds of connections are registered: browsing websites, scrolling through social media, video calling with friends, and so on. However, these `human' labels are usually not stored by the computer. And, simply due to time restrictions, it is not possible for a cyber expert to give an interpretation to all the traffic. Fortunately, over the past decades, the field of data science has grown at lightning speed. Often Machine Learning (ML) is used to extract information from all kinds of data. ML includes mathematical algorithms that learn patterns in data and that can make predictions about it. Since a computer can perform calculations extremely much faster, huge datasets can be interpreted in considerably less time. Typically ML has difficulty recognising patterns of data that is has not been able to learn from before, though. People, however, are good at assigning meaning to deviating data points by using domain knowledge. Therefore, we investigate how we can combine human knowledge and computer power to arrive at better and understandable predictions. This thesis focuses on molding the symbiosis between human and machine. We consider the following three themes: Anomaly Detection, Model Evaluation and Active Learning. Anomaly detection algorithms are widely used within network intrusion detection, the field that deals with finding cyber attacks in network data. In Chapters 2 and 3, we apply algorithms to detect malicious points in, respectively, real cyber data and data with online aeroplane bookings that contain fraudulent reservations. First, we remove redundant or uninformative data features and create new variables by using domain knowledge and by combining or standardising other features. In addition, we demonstrate how we deal with unlabelled and partially labelled data. Finally, we involve human experts by having them evaluate the results. By having the experts assess data points in a targeted manner, we efficiently obtain an indication of the predictive power of ML algorithms. Moreover, malicious data can be found faster. It is difficult to determine the predictive power of an ML model when fully labelled evaluation data is insufficiently available. Therefore, in Chapter 4 we develop a new evaluation metric that provides a good and robust estimate of the predictive power when no negative labels are available. In Chapter 5, we consider Model Evaluation more fundamentally using a common problem that arises when evaluating an algorithm. Often an evaluation metric is used to determine predictive power. For example, a metric score of 80% sounds good, but on what is this feeling based? Therefore, we introduce the Dutch Draw as a method to generate general, simple, but informative baselines. In the Active Learning (AL) paradigm, the human expert becomes an integral part of the methodology as a labelling source. AL is important in fields where labelled data is relatively scarce. In Chapters 6 and 7, we develop new AL methods for network intrusion detection. The first method, Jasmine, has a dynamic selection procedure built in. This means that unlabelled data is given meaning in a more effective manner, allowing malicious network traffic to be discovered at an earlier stage. In Chapter 7, we improve the Jasmine methodology and extend it to Plusmine. We refine the dynamic selection procedure and we include Automatic Classification (AC) that increases the speed of labelling. An important advantage of our method is that it works relatively simply and therefore requires little extra computational resources

Analysis of Two-Body Decays of Charmed Baryons Using the Quark-Diagram Scheme

We give a general formulation of the quark-diagram scheme for the nonleptonic weak decays of baryons. We apply it to all the decays of the antitriplet and sextet charmed baryons and express their decay amplitudes in terms of the quark-diagram amplitudes. We have also given parametrizations for the effects of final-state interactions. For SU(3) violation effects, we only parametrize those in the horizontal $W$-loop quark diagrams whose contributions are solely due to SU(3)-violation effects. In the absence of all these effects, there are many relations among various decay modes. Some of the relations are valid even in the presence of final-state interactions when each decay amplitude in the relation contains only a single phase shift. All these relations provide useful frameworks to compare with future experiments and to find out the effects of final-state interactions and SU(3) symmetry violations.Comment: 28 pages, 20 Tables in landscape form, 4 figures. Main changes are: (i) some errors in the Tables and in the relations between the quark-diagram amplitudes of this paper and those of Ref.[10] are corrected, (ii) improvements are made in the presentation so that comparisons with previous works and what have been done to include SU(3) breaking and final-state interactions are more clearly stated; to appear in the Physical Review

Cooperative WebLab in chemical engineering between France and Brazil: Validation of the methodology

A WebLab is an experiment operated remotely via Internet. Besides the strictly technical aspects of such an experiment, which may contribute to the learning of Chemical Engineering fundamentals, there is also important feedback when teams of students of two different countries are working together: the WebLab becomes an intercultural experience, enhancing the communication skills of the students. A WebLab between Universidade Federal de São Carlos (DEQ/UFSCar) and the Ecole Nationale Supérieure d’Ingénieurs en Arts Chimiques et Technologiques (ENSIACET) is presented in this work. A mass transfer experiment in a bench scale reactor (stirred and aerated) had to be studied by mixed teams, thus emulating challenges that will be common in future working environments. In order to perform the experiment, students in Brazil and in France were put into groups. The students had to make decisions about the procedure for executing the experiments. All the students were able to control the equipment, no matter where they were physically. Students communicated using video conference software. The students' and teachers' opinions of this experience were very positive. This methodology is an important contribution to the education of engineers in a world integrated by modern communication technologies

Infrared dermal thermography on diabetic feet soles to predict ulcerations: a case study

Diabetic foot ulceration is a major complication for patients with diabetes mellitus. If not adequately treated, these ulcers may lead to foot infection, and ultimately to lower extremity amputation, which imposes a major burden to society and great loss in health-related quality of life for patients. Early identification and subsequent preventive treatment have proven useful to limit the incidence of foot ulcers and lower extremity amputation. Thus, the development of new diagnosis tools has become an attractive option. The ultimate objective of our project is to develop an intelligent telemedicine monitoring system for frequent examination on patients’ feet, to timely detect pre-signs of ulceration. Inflammation in diabetic feet can be an early and predictive warning sign for ulceration, and temperature has been proven to be a vicarious marker for inflammation. Studies have indicated that infrared dermal thermography of foot soles can be one of the important parameters for assessing the risk of diabetic foot ulceration. This paper covers the feasibility study of using an infrared camera, FLIR SC305, in our setup, to acquire the spatial thermal distribution on the feet soles. With the obtained thermal images, automated detection through image analysis was performed to identify the abnormal increased/decreased temperature and assess the risk for ulceration. The thermography for feet soles of patients with diagnosed diabetic foot complications were acquired before the ordinary foot examinations. Assessment from clinicians and thermography were compared and follow-up measurements were performed to investigate the prediction. A preliminary case study will be presented, indicating that dermal thermography in our proposed setup can be a screening modality to timely detect pre-signs of ulceration

A schematic model for QCD I: Low energy meson states

A simple model for QCD is presented, which is able to reproduce the meson spectrum at low energy. The model is a Lipkin type model for quarks coupled to gluons. The basic building blocks are pairs of quark-antiquarks coupled to a definite flavor and spin. These pairs are coupled to pairs of gluons with spin zero. The multiplicity problem, which dictates that a given experimental state can be described in various manners, is removed when a particle-mixing interaction is turned on. In this first paper of a series we concentrates on the discussion of meson states at low energy, the so-called zero temperature limit of the theory. The treatment of baryonic states is indicated, also.Comment: 29 pages, 6 figures. submitted to Phys. Rev.

Real-Time Onboard Global Nonlinear Aerodynamic Modeling from Flight Data

Flight test and modeling techniques were developed to accurately identify global nonlinear aerodynamic models onboard an aircraft. The techniques were developed and demonstrated during piloted flight testing of an Aermacchi MB-326M Impala jet aircraft. Advanced piloting techniques and nonlinear modeling techniques based on fuzzy logic and multivariate orthogonal function methods were implemented with efficient onboard calculations and flight operations to achieve real-time maneuver monitoring and analysis, and near-real-time global nonlinear aerodynamic modeling and prediction validation testing in flight. Results demonstrated that global nonlinear aerodynamic models for a large portion of the flight envelope were identified rapidly and accurately using piloted flight test maneuvers during a single flight, with the final identified and validated models available before the aircraft landed

Early stage scaling in phase ordering kinetics

A global analysis of the scaling behaviour of a system with a scalar order parameter quenched to zero temperature is obtained by numerical simulation of the Ginzburg-Landau equation with conserved and non conserved order parameter. A rich structure emerges, characterized by early and asymptotic scaling regimes, separated by a crossover. The interplay among different dynamical behaviours is investigated by varying the parameters of the quench and can be interpreted as due to the competition of different dynamical fixed points.Comment: 21 pages, latex, 7 figures available upon request from [email protected]