Towards predicting Pedestrian Evacuation Time and Density from Floorplans using a Vision Transformer

Conventional pedestrian simulators are inevitable tools in the design process of a building, as they enable project engineers to prevent overcrowding situations and plan escape routes for evacuation. However, simulation runtime and the multiple cumbersome steps in generating simulation results are potential bottlenecks during the building design process. Data-driven approaches have demonstrated their capability to outperform conventional methods in speed while delivering similar or even better results across many disciplines. In this work, we present a deep learning-based approach based on a Vision Transformer to predict density heatmaps over time and total evacuation time from a given floorplan. Specifically, due to limited availability of public datasets, we implement a parametric data generation pipeline including a conventional simulator. This enables us to build a large synthetic dataset that we use to train our architecture. Furthermore, we seamlessly integrate our model into a BIM-authoring tool to generate simulation results instantly and automatically

Ειδική για τον ασθενή μοντελοποίηση, προσομοίωση και επεξεργασία σε πραγματικό χρόνο για ασθένειες του αναπνευστικού συστήματος

Asthma is a common chronic disease of the respiratory system causing significant disability and societal burden. It affects more than 300 million more people worldwide, while more than 100 million people will likely have asthma by 2025. The price of asthma varies greatly from nation to nation. Mean yearly cost can be estimated to 1900 EUR in Europe and $3100 in the United States. Managing asthma involves controlling symptoms, preventing exacerbations, and maintaining lung function. Improved asthma control is reduces the risk of exacerbations and lung function impairment while reducing the direct costs of asthma care and indirect costs associated with reduced productivity. Understanding the complex dynamics of the pulmonary system and the lung's response to disease is fundamental to the advancement of Asthma treatment. Computational models of the respiratory system seek to provide a theoretical framework to understand the interaction between structure and function. Their application can improve pulmonary medicine by a patient-specific approach to medicinal methodologies optimizing the delivery given the personalized geometry and personalized ventilation patterns. A three-fold objective is addressed within this dissertation. The first part refers to the comprehension of pulmonary pathophysiology and the mechanics of Asthma and subsequently of constrictive pulmonary conditions in general. The second part refers to the design and implementation of tools that facilitate personalized medicine to improve delivery and effectiveness. Finally, the third part refers to the self-management of the condition, meaning that medical personnel and patients have access to tools and methods that allow the first party to easily track the course of the condition and the second party, i.e. the patient to easily self-manage it alleviating the significant burden from the health system.Το άσθμα είναι μια χρόνια νόσος του αναπνευστικού συστήματος που προκαλεί σημαντική δυσχέρια και οικονομική επιβάρυνση. Επηρεάζει περισσότερους από 300 εκατομμύρια ανθρώπους παγκοσμίως, ενώ περισσότεροι από 100 εκατομμύρια άνθρωποι επιπλέον πιθανότατα θα έχουν άσθμα μέχρι το 2025. Η τιμή του άσθματος ποικίλλει πολύ από χώρα σε χώρα. Το μέσο ετήσιο κόστος μπορεί να εκτιμηθεί σε 1900 EUR στην Ευρώπη και 3100$ στις Ηνωμένες Πολιτείες. Η διαχείριση του άσθματος περιλαμβάνει τον έλεγχο των συμπτωμάτων, την πρόληψη των παροξύνσεων και τη διατήρηση της πνευμονικής λειτουργίας. Ο βελτιωμένος έλεγχος του άσθματος μειώνει τον κίνδυνο παροξύνσεων και εξασθένησης της πνευμονικής λειτουργίας, ενώ μειώνει το άμεσο κόστος της φροντίδας του άσθματος και το έμμεσο κόστος που σχετίζεται με μειωμένη παραγωγικότητα. Η κατανόηση της πολύπλοκης δυναμικής του αναπνευστικού συστήματος και της ανταπόκρισης του πνεύμονα στην ασθένεια είναι θεμελιώδης για την πρόοδο της θεραπείας του άσθματος. Τα υπολογιστικά μοντέλα του αναπνευστικού συστήματος επιδιώκουν να παρέχουν ένα θεωρητικό πλαίσιο για την κατανόηση της αλληλεπίδρασης μεταξύ δομής και λειτουργίας. Η εφαρμογή τους μπορεί να βελτιώσει την ιατρική του αναπνευστικού συστήματος, μέσω μιας ειδικής για τον ασθενή προσέγγισης σε ιατρικές μεθοδολογίες που βελτιστοποιούν την παροχή δεδομένων της εξατομικευμένης γεωμετρίας και των εξατομικευμένων μοτίβων κυκλοφορίας του αέρα. Στην παρούσα διατριβή εξετάζεται ένας τριπλός στόχος. Το πρώτο μέρος αναφέρεται στην κατανόηση της πνευμονικής παθοφυσιολογίας και της μηχανικής του άσθματος και στη συνέχεια των συσταλτικών πνευμονικών καταστάσεων γενικότερα. Το δεύτερο μέρος αναφέρεται στον σχεδιασμό και την εφαρμογή εργαλείων που διευκολύνουν την εξατομικευμένη ιατρική για τη βελτίωση της παροχής και της αποτελεσματικότητας. Τέλος, το τρίτο μέρος αναφέρεται στην αυτοδιαχείριση της πάθησης, που σημαίνει ότι το ιατρικό προσωπικό και οι ασθενείς έχουν πρόσβαση σε εργαλεία και μεθόδους που επιτρέπουν στο πρώτο μέρος να παρακολουθεί εύκολα την πορεία της πάθησης και στο δεύτερο μέρος, δηλαδή στον ασθενή, να αυτοδιαχειριζεται τα συμπτώματα ελαφρύνοντας σημαντικά την επιβάρυνση στο σύστημα υγείας

AVATREE: An open-source computational modelling framework modelling Anatomically Valid Airway TREE conformations.

This paper presents AVATREE, a computational modelling framework that generates Anatomically Valid Airway tree conformations and provides capabilities for simulation of broncho-constriction apparent in obstructive pulmonary conditions. Such conformations are obtained from the personalized 3D geometry generated from computed tomography (CT) data through image segmentation. The patient-specific representation of the bronchial tree structure is extended beyond the visible airway generation depth using a knowledge-based technique built from morphometric studies. Additional functionalities of AVATREE include visualization of spatial probability maps for the airway generations projected on the CT imaging data, and visualization of the airway tree based on local structure properties. Furthermore, the proposed toolbox supports the simulation of broncho-constriction apparent in pulmonary diseases, such as chronic obstructive pulmonary disease (COPD) and asthma. AVATREE is provided as an open-source toolbox in C++ and is supported by a graphical user interface integrating the modelling functionalities. It can be exploited in studies of gas flow, gas mixing, ventilation patterns and particle deposition in the pulmonary system, with the aim to improve clinical decision making

Accelerating Deep Neural Networks for Efficient Scene Understanding in Multi-Modal Automotive Applications

Environment perception constitutes one of the most critical operations performed by semi- and fully- autonomous vehicles. In recent years, Deep Neural Networks (DNNs) have become the standard tool for perception solutions owing to their impressive capabilities in analyzing and modelling complex and dynamic scenes, from (often multi-modal) sensory inputs. However, the well-established performance of DNNs comes at the cost of increased time and storage complexity, which may become problematic in automotive perception systems due to the requirement for a short prediction horizon (as in many cases inference must be performed in real-time) and the limited computational, storage, and energy resources of mobile systems. A common way of addressing this problem is to transform the original large pre-trained networks into new smaller models, by utilizing Model Compression and Acceleration (MCA) techniques, improving both their storage and execution efficiency. Within the MCA framework, in this paper, we investigate the application of two state-of-the-art weight-sharing MCA techniques, namely a Vector Quantization (VQ) and a Dictionary Learning (DL) one, as well as two novel extensions, towards the acceleration and compression of widely used DNNs for 2D and 3D object-detection in automotive applications. Apart from the individual (uni-modal) networks, we also present and evaluate a multi-modal late-fusion algorithm for combining the detection results of the 2D and 3D detectors. Our evaluation studies are carried out on the KITTI Dataset. The obtained results lend themselves to a twofold interpretation. On the one hand, they showcase the significant acceleration and compression gains that can be achieved via the application of weight sharing on the selected DNN detectors, with limited accuracy loss, as well as highlight the performance differences between the two utilized weight-sharing approaches. On the other, they demonstrate the substantial boost in detection performance obtained by combining the outcome of the two unimodal individual detectors, using the proposed late-fusion-based multi-modal approach. Indeed, as our experiments have shown, pairing the high-performance DL-based MCA technique with the loss-mitigating effect of the multi-modal fusion approach, leads to highly accelerated models (up to approximately $2.5 \times$ and $6\times$ for the 2D and 3D detectors, respectively) with the performance loss of the fused results ranging in most cases within single-digits figures (as low as around 1% for the class “cars”)

Uncertainty Management for Wearable IoT Wristband Sensors Using Laplacian-Based Matrix Completion

Contemporary sensing devices provide reliable mechanisms for continuous process monitoring, accommodating use cases related to mHealth and smart mobility, by generating real-time data streams of numerous physiological and vital parameters. Such data streams can be later utilized by machine learning algorithms and decision support systems to predict critical clinical states and motivate users to adopt behaviours that improve the quality of their life and the society as a whole. However, in many cases, even when deployed over highly sophisticated, cutting-edge network infrastructure and deployment paradigms, data may exhibit missing values and non-uniformities due to various reasons, including device malfunction, deliberate data reduction for efficient processing, or data loss due to sensing and communication failures. This work proposes a novel approach to deal with missing entries in heart rate measurements. Benefiting from the low-rank property of the generated data matrices and the proximity of neighbouring measurements, we provide a novel method that combines classical matrix completion approaches with weighted Laplacian interpolation offering high reconstruction accuracy at fast execution times. Extensive evaluation studies carried out with real measurements show that the proposed methods could be effectively deployed by modern wristband-cloud computing systems increasing the robustness, the reliability and the energy efficiency of these systems

Coping with missing data in an unobtrusive monitoring system for office workers

Current trend of population ageing at global level is accompanied by increased prevalence of chronic diseases and higher rates of early retirement and labor market exit. In particular, the lifestyle of office workers is characterized by prolonged sitting and overall sedentary life, which alone is a high risk factor for cardiometabolic diseases, obesity and other related chronic diseases. The SmartWork unobtrusive monitoring system allows for continuous monitoring of various lifestyle, health, behavioural and work related parameters of office workers targeting to empower work ability sustainability. The large amounts of collected data in such systems are often characterized by the presence of missing entries. This work is an exploratory study on the potential of a Laplacian matrix completion variant for data imputation on the multi-channel time-series data collected with wearable or work devices in the SmartWork system