Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques

Diabetic foot ulcers (DFUs) constitute a serious complication for people with diabetes. The care of DFU patients can be substantially improved through self-management, in order to achieve early-diagnosis, ulcer prevention, and complications management in existing ulcers. In this paper, we investigate two categories of image-to-image translation techniques (ItITT), which will support decision making and monitoring of diabetic foot ulcers: noise reduction and super-resolution. In the former case, we investigated the capabilities on noise removal, for convolutional neural network stacked-autoencoders (CNN-SAE). CNN-SAE was tested on RGB images, induced with Gaussian noise. The latter scenario involves the deployment of four deep learning super-resolution models. The performance of all models, for both scenarios, was evaluated in terms of execution time and perceived quality. Results indicate that applied techniques consist a viable and easy to implement alternative that should be used by any system designed for DFU monitoring

Automatic 3D modeling and reconstruction of cultural heritage sites from Twitter images

This paper presents an approach for leveraging the abundance of images posted on social media like Twitter for large scale 3D reconstruction of cultural heritage landmarks. Twitter allows users to post short messages, including photos, describing a plethora of activities or events, e.g., tweets are used by travelers on vacation, capturing images from various cultural heritage assets. As such, a great number of images are available online, able to drive a successful 3D reconstruction process. However, reconstruction of any asset, based on images mined from Twitter, presents several challenges. There are three main steps that have to be considered: (i) tweets’ content identification, (ii) image retrieval and filtering, and (iii) 3D reconstruction. The proposed approach first extracts key events from unstructured tweet messages and then identifies cultural activities and landmarks. The second stage is the application of a content-based filtering method so that only a small but representative portion of cultural images are selected to support fast 3D reconstruction. The proposed methods are experimentally evaluated using real-world data and comparisons verify the effectiveness of the proposed scheme.peer-reviewe

Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals

Detection of outliers in radar signals is a considerable challenge in maritime surveillance applications. High-Frequency Surface-Wave (HFSW) radars have attracted significant interest as potential tools for long-range target identification and outlier detection at over-the-horizon (OTH) distances. However, a number of disadvantages, such as their low spatial resolution and presence of clutter, have a negative impact on their accuracy. In this paper, we explore the applicability of deep learning techniques for detecting deviations from the norm in behavioral patterns of vessels (outliers) as they are tracked from an OTH radar. The proposed methodology exploits the nonlinear mapping capabilities of deep stacked autoencoders in combination with density-based clustering. A comparative experimental evaluation of the approach shows promising results in terms of the proposed methodology’s performance

Linear stability analysis of hypersonic boundary layers computed by a kinetic approach: a semi-infinite flat plate at 4.5 <= M-infinity <= 9

Linear stability analysis is performed using a combination of two-dimensional Direct Simulation Monte Carlo (DSMC) method for the computation of the basic state and solution of the pertinent eigenvalue problem, as applied to the canonical boundary layer on a semi-infinite flat plate. Three different gases are monitored, namely nitrogen, argon and air, the latter as a mixture of 79\% Nitrogen and 21\% Oxygen at a range of free-stream Mach numbers corresponding to flight at an altitude of 55km. A neural network has been utilised to predict and smooth the raw DSMC data; the steady laminar profiles obtained are in very good agreement with those computed by (self-similar) boundary layer theory, under isothermal or adiabatic wall conditions, subject to the appropriate slip corrections computed in the DSMC method. The leading eigenmode results pertaining to the unsmoothed DSMC profiles are compared against those of the classic boundary layer theory. Small quantitative, but no significant qualitative differences between the results of the two classes of steady base flows have been found at all parameters examined. The frequencies of the leading eigenmodes at all conditions examined are practically identical, while perturbations corresponding to the DSMC profiles are found to be systematically more damped than their counterparts arising in the boundary layer at the conditions examined, when the correct velocity slip and temperature jump boundary conditions are imposed in the base flow profiles; by contrast, when the classic no-slip boundary conditions are used, less damped/more unstable profiles are obtained, which would lead the flow to earlier transition. On the other hand, the DSMC profiles smoothed by the neural network are marginally more stable than their unsmoothed counterparts

Τεχνικές μάθησης μερικής επίβλεψης για λήψη αποφάσεων

Semi-supervised learning (SSL) is a class of supervised learning tasks and techniques that also exploits the unlabeled data for training. SSL significantly reduces labeling related costs and is able to handle large data sets. The primary objective is the extraction of robust inference rules.Decision support systems (DSSs) who utilize SSL have significant advantages. Only a small amount of labelled data is required for the initialization. Then, new (unlabeled) data can be utilized and improve system’s performance. Thus, the DSS is continuously adopted to new conditions, with minimum effort. Techniques which are cost effective and easily adopted to dynamic systems, can be beneficial for many practical applications. Such applications fields are: (a) industrial assembly lines monitoring, (b) sea border surveillance, (c) elders’ falls detection, (d) transportation tunnels inspection, (e) concrete foundation piles defect recognition, (f) commercial sector companies financial assessment and (g) image advanced filtering for cultural heritage applications.Ο όρος μάθηση με μερική επίβλεψη αναφέρεται σε ένα ευρύ πεδίο τεχνικών μηχανικής μάθησης, οι οποίες χρησιμοποιούν τα μη τιτλοφορημένα δεδομένα για να εξάγουν επιπλέον ωφέλιμη πληροφορία. Η μερική επίβλεψη αντιμετωπίζει προβλήματα που σχετίζονται με την επεξεργασία και την αξιοποίηση μεγάλου όγκου δεδομένων και τα όποια κόστη σχετίζονται με αυτά (π.χ. χρόνος επεξεργασίας, ανθρώπινα λάθη). Απώτερος σκοπός είναι η ασφαλή εξαγωγή συμπερασμάτων, κανόνων ή προτάσεων. Τα μοντέλα λήψης απόφασης που χρησιμοποιούν τεχνικές μερικής μάθησης έχουν ποικίλα πλεονεκτήματα. Σε πρώτη φάση, χρειάζονται μικρό πλήθος τιτλοφορημένων δεδομένων για την αρχικοποίηση τους. Στη συνέχεια, τα νέα δεδομένα που θα εμφανιστούν αξιοποιούνται και τροποποιούν κατάλληλα το μοντέλο. Ως εκ τούτου, έχουμε ένα συνεχώς εξελισσόμενο μοντέλο λήψης αποφάσεων, με την ελάχιστη δυνατή προσπάθεια.Τεχνικές που προσαρμόζονται εύκολα και οικονομικά είναι οι κατεξοχήν κατάλληλες για τον έλεγχο συστημάτων, στα οποία παρατηρούνται συχνές αλλαγές στον τρόπο λειτουργίας. Ενδεικτικά πεδία εφαρμογής εφαρμογής ευέλικτων συστημάτων υποστήριξης λήψης αποφάσεων με μερική μάθηση είναι: η επίβλεψη γραμμών παραγωγής, η επιτήρηση θαλάσσιων συνόρων, η φροντίδα ηλικιωμένων, η εκτίμηση χρηματοπιστωτικού κινδύνου, ο έλεγχος για δομικές ατέλειες και η διαφύλαξη της πολιτιστικής κληρονομιάς