Tags:
Programmering,
Object Oriented programming,
distributed computing,
design patterns
Publications
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Reinholdt, Jonas Paludan; Jul, Eric Bartley & Ferreira, Paulo
(2024).
flyDetect: An Android Application for Flight Detection.
Sensors.
24(18).
doi:
10.3390/s24186158.
Full text in Research Archive
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Over the past years, transport mode recognition has become a large field of research. However, flight as a type of transportation has been mostly overlooked. A system for flight detection might be useful for context-aware applications, but more importantly, it can be used to automatically manage airplane mode on smartphones. Smartphones transmit radio frequency signals which could potentially interfere with aircraft systems, and it is therefore important that devices enable airplane mode to avoid this problem. This paper proposes flyDetect, a method for automatic flight mode detection and an embodiment in the form of an app that demonstrates the viability of the method. Thus, the system uses the accelerometer and barometer in an Android smartphone, can detect the start and end of a flight, and notify other apps or systems on the device when this happens. Our evaluation shows that flyDetect meets the requirements set for the solution, and the results are very promising.
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Bentsen, Pål Andreas Hoven; Hicks, Steven; Jul, Eric Bartley; Halvorsen, Pål & Thambawita, Vajira L B
(2024).
SAM in the Pipeline: Transforming Axis-Aligned to Oriented Bounding Boxes for Superior Sperm Detection.
In Guðmundsson, Gylfi Þór; Amsaleg, Laurent; Chetouani, Adadine; Jonsson, Bjorn Por; Gennaro, Claudio & Kijak, Ewa (Ed.),
CBMI 24: Proceedings of the 2024 International Conference on Content-Based Multimedia Indexing.
IEEE (Institute of Electrical and Electronics Engineers).
ISSN 9798350378443.
doi:
10.1109/CBMI62980.2024.10859223.
Full text in Research Archive
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Kamalian, Mahdieh; Ferreira, Paulo & Jul, Eric Bartley
(2022).
A survey on local transport mode detection on the edge of the network.
Applied Intelligence - The International Journal of Research on Intelligent Systems for Real Life Complex Problems.
ISSN 0924-669X.
52(14),
p. 16021–16050.
doi:
10.1007/s10489-022-03214-y.
Full text in Research Archive
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We present a survey of smartphone-based Transport Mode Detection (TMD). We categorize TMD solutions into local and
remote; the first ones are addressed in this article. A local approach performs the following steps in the smartphone (and not
in some faraway cloud servers): 1) data collection or sensing, 2) preprocessing, 3) feature extraction, and 4) classification
(with a previous training phase). A local TMD approach outperforms a remote approach due to less delay, improved
privacy, no need for Internet connection, better or equal accuracy and smaller data size. Therefore, we present local TMD
solutions taking into account the above mentioned four steps and analyze them according to the most relevant requirements:
accuracy, delay, resources consumption and generalization. To achieve the highest accuracy (100%), studies used a different
combination of sensors, features and Machine Learning (ML) algorithms. The results suggest that accelerometer and GPS
(Global Position System) are the most useful sensors for data collection. Discriminative ML algorithms, such as random
forest, outperform the other algorithms for classification. Some solutions improved the delay of the proposed system by
using a small window size and a local approach. A few studies could improve battery usage of their system by utilizing low
battery-consuming sensors (e.g., accelerometer) and low sampling rate (e.g., 10Hz). CPU usage is primarily dependent on
data collection, while memory usage is related to the features and complexity of the ML algorithm. Finally, the generalization
requirement is met in studies that consider user, location and position independency into account.
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Helmy, Maged; Truong, Trung Tuyen; Dykyy, Anastasiya; Ferreira, Paulo & Jul, Eric Bartley
(2022).
CapillaryNet: An automated system to quantify skin capillary density and red blood cell velocity from handheld vital microscopy.
Artificial Intelligence in Medicine.
ISSN 0933-3657.
127.
doi:
10.1016/j.artmed.2022.102287.
Full text in Research Archive
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Capillaries are the smallest vessels in the body which are responsible for delivering oxygen and nutrients to surrounding cells. Various life-threatening diseases are known to alter the density of healthy capillaries and the flow velocity of erythrocytes within the capillaries. In previous studies, capillary density and flow velocity were manually assessed by trained specialists. However, manual analysis of a standard 20-s microvascular video requires 20 min on average and necessitates extensive training. Thus, manual analysis has been reported to hinder the application of microvascular microscopy in a clinical environment. To address this problem, this paper presents a fully automated state-of-the-art system to quantify skin nutritive capillary density and red blood cell velocity captured by handheld-based microscopy videos. The proposed method combines the speed of traditional computer vision algorithms with the accuracy of convolutional neural networks to enable clinical capillary analysis. The results show that the proposed system fully automates capillary detection with an accuracy exceeding that of trained analysts and measures several novel microvascular parameters that had eluded quantification thus far, namely, capillary hematocrit and intracapillary flow velocity heterogeneity. The proposed end-to-end system, named CapillaryNet, can detect capillaries at ~0.9 s per frame with ~93% accuracy. The system is currently used as a clinical research product in a larger e-health application to analyse capillary data captured from patients suffering from COVID-19, pancreatitis, and acute heart diseases. CapillaryNet narrows the gap between the analysis of microcirculation images in a clinical environment and state-of-the-art systems.
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Helmy, Maged; Truong, Trung Tuyen; Jul, Eric Bartley & Ferreira, Paulo
(2022).
Deep Learning and Computer Vision Techniques for
Microcirculation Analysis: A Review.
Patterns.
4(1).
doi:
10.1016/j.patter.2022.100641.
Full text in Research Archive
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The analysis of microcirculation images has the potential to reveal early signs of life-threatening diseases such as sepsis. Quantifying the capillary density and the capillary distribution in microcirculation images can be used as a biological marker to assist critically ill patients. The quantification of these biological markers is labor intensive, time consuming, and subject to interobserver variability. Several computer vision techniques with varying performance can be used to automate the analysis of these microcirculation images in light of the stated challenges. In this paper, we present a survey of over 50 research papers and present the most relevant and promising computer vision algorithms to automate the analysis of microcirculation images. Furthermore, we present a survey of the methods currently used by other researchers to automate the analysis of microcirculation images. This survey is of high clinical relevance because it acts as a guidebook of techniques for other researchers to develop their microcirculation analysis systems and algorithms.
View all works in NVA
Published
Nov. 4, 2010 2:00 PM
- Last modified
Oct. 20, 2025 9:02 AM
