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Recently, a growing number of Higher Education institutions have started to implement challenge-based learning (CBL) in study processes. However, despite the growing Higher Education attention to challenge-based learning, research on the method, especially in Engineering education, has not been extensively conducted and made publicly available to the community of researchers and teaching practitioners. To bridge this gap, this paper provides a case analysis of implementing challenge-based learning in a Master’s degree program for engineering students, aiming to highlight the main aspects of combining challenge-based learning and Sustainable Development Goal 11 (SDG 11), namely sustainable cities and communities. The findings are consistent with previous CBL studies revealing positive benefits of implementing the method; however, the paper adds novelty by showcasing the learning pathways that emerge to learners and teachers when CBL is implemented in an SDG-11-focused course.
Daina Gudonienė; Agnė Paulauskaitė-Tarasevičienė; Asta Daunorienė; Vilma Sukackė. A Case Study on Emerging Learning Pathways in SDG-Focused Engineering Studies through Applying CBL. Sustainability 2021, 13, 8495 .
AMA StyleDaina Gudonienė, Agnė Paulauskaitė-Tarasevičienė, Asta Daunorienė, Vilma Sukackė. A Case Study on Emerging Learning Pathways in SDG-Focused Engineering Studies through Applying CBL. Sustainability. 2021; 13 (15):8495.
Chicago/Turabian StyleDaina Gudonienė; Agnė Paulauskaitė-Tarasevičienė; Asta Daunorienė; Vilma Sukackė. 2021. "A Case Study on Emerging Learning Pathways in SDG-Focused Engineering Studies through Applying CBL." Sustainability 13, no. 15: 8495.
There are many tool condition monitoring solutions that use a variety of sensors. This paper presents a self-powering wireless sensor node for shank-type rotating tools and a method for real-time end mill wear monitoring. The novelty of the developed and patented sensor node is that the longitudinal oscillations, which directly affect the intensity of the energy harvesting, are significantly intensified due to the helical grooves cut onto the conical surface of the tool holder horn. A wireless transmission of electrical impulses from the capacitor is proposed, where the collected electrical energy is charged and discharged when a defined potential is reached. The frequency of the discharge pulses is directly proportional to the wear level of the tool and, at the same time, to the surface roughness of the workpiece. By employing these measures, we investigate the support vector machine (SVM) approach for wear level prediction.
Vytautas Ostasevicius; Paulius Karpavicius; Agne Paulauskaite-Taraseviciene; Vytautas Jurenas; Arkadiusz Mystkowski; Ramunas Cesnavicius; Laura Kizauskiene. A Machine Learning Approach for Wear Monitoring of End Mill by Self-Powering Wireless Sensor Nodes. Sensors 2021, 21, 3137 .
AMA StyleVytautas Ostasevicius, Paulius Karpavicius, Agne Paulauskaite-Taraseviciene, Vytautas Jurenas, Arkadiusz Mystkowski, Ramunas Cesnavicius, Laura Kizauskiene. A Machine Learning Approach for Wear Monitoring of End Mill by Self-Powering Wireless Sensor Nodes. Sensors. 2021; 21 (9):3137.
Chicago/Turabian StyleVytautas Ostasevicius; Paulius Karpavicius; Agne Paulauskaite-Taraseviciene; Vytautas Jurenas; Arkadiusz Mystkowski; Ramunas Cesnavicius; Laura Kizauskiene. 2021. "A Machine Learning Approach for Wear Monitoring of End Mill by Self-Powering Wireless Sensor Nodes." Sensors 21, no. 9: 3137.
Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.
Vidas Raudonis; Agne Paulauskaite-Taraseviciene; Kristina Sutiene. Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement. Sensors 2021, 21, 863 .
AMA StyleVidas Raudonis, Agne Paulauskaite-Taraseviciene, Kristina Sutiene. Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement. Sensors. 2021; 21 (3):863.
Chicago/Turabian StyleVidas Raudonis; Agne Paulauskaite-Taraseviciene; Kristina Sutiene. 2021. "Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement." Sensors 21, no. 3: 863.
(1) Background: The segmentation of cell nuclei is an essential task in a wide range of biomedical studies and clinical practices. The full automation of this process remains a challenge due to intra- and internuclear variations across a wide range of tissue morphologies, differences in staining protocols and imaging procedures. (2) Methods: A deep learning model with metric embeddings such as contrastive loss and triplet loss with semi-hard negative mining is proposed in order to accurately segment cell nuclei in a diverse set of microscopy images. The effectiveness of the proposed model was tested on a large-scale multi-tissue collection of microscopy image sets. (3) Results: The use of deep metric learning increased the overall segmentation prediction by 3.12% in the average value of Dice similarity coefficients as compared to no metric learning. In particular, the largest gain was observed for segmenting cell nuclei in H&E -stained images when deep learning network and triplet loss with semi-hard negative mining were considered for the task. (4) Conclusion: We conclude that deep metric learning gives an additional boost to the overall learning process and consequently improves the segmentation performance. Notably, the improvement ranges approximately between 0.13% and 22.31% for different types of images in the terms of Dice coefficients when compared to no metric deep learning.
Tomas Iesmantas; Agne Paulauskaite-Taraseviciene; Kristina Sutiene. Enhancing Multi-tissue and Multi-scale Cell Nuclei Segmentation with Deep Metric Learning. Applied Sciences 2020, 10, 615 .
AMA StyleTomas Iesmantas, Agne Paulauskaite-Taraseviciene, Kristina Sutiene. Enhancing Multi-tissue and Multi-scale Cell Nuclei Segmentation with Deep Metric Learning. Applied Sciences. 2020; 10 (2):615.
Chicago/Turabian StyleTomas Iesmantas; Agne Paulauskaite-Taraseviciene; Kristina Sutiene. 2020. "Enhancing Multi-tissue and Multi-scale Cell Nuclei Segmentation with Deep Metric Learning." Applied Sciences 10, no. 2: 615.
Background Infertility and subfertility affect a significant proportion of humanity. Assisted reproductive technology has been proven capable of alleviating infertility issues. In vitro fertilisation is one such option whose success is highly dependent on the selection of a high-quality embryo for transfer. This is typically done manually by analysing embryos under a microscope. However, evidence has shown that the success rate of manual selection remains low. The use of new incubators with integrated time-lapse imaging system is providing new possibilities for embryo assessment. As such, we address this problem by proposing an approach based on deep learning for automated embryo quality evaluation through the analysis of time-lapse images. Automatic embryo detection is complicated by the topological changes of a tracked object. Moreover, the algorithm should process a large number of image files of different qualities in a reasonable amount of time. Methods We propose an automated approach to detect human embryo development stages during incubation and to highlight embryos with abnormal behaviour by focusing on five different stages. This method encompasses two major steps. First, the location of an embryo in the image is detected by employing a Haar feature-based cascade classifier and leveraging the radiating lines. Then, a multi-class prediction model is developed to identify a total cell number in the embryo using the technique of deep learning. Results The experimental results demonstrate that the proposed method achieves an accuracy of at least 90% in the detection of embryo location. The implemented deep learning approach to identify the early stages of embryo development resulted in an overall accuracy of over 92% using the selected architectures of convolutional neural networks. The most problematic stage was the 3-cell stage, presumably due to its short duration during development. Conclusion This research contributes to the field by proposing a model to automate the monitoring of early-stage human embryo development. Unlike in other imaging fields, only a few published attempts have involved leveraging deep learning in this field. Therefore, the approach presented in this study could be used in the creation of novel algorithms integrated into the assisted reproductive technology used by embryologists.
Vidas Raudonis; Agne Paulauskaite-Taraseviciene; Kristina Sutiene; Domas Jonaitis. Towards the automation of early-stage human embryo development detection. BioMedical Engineering OnLine 2019, 18, 1 -20.
AMA StyleVidas Raudonis, Agne Paulauskaite-Taraseviciene, Kristina Sutiene, Domas Jonaitis. Towards the automation of early-stage human embryo development detection. BioMedical Engineering OnLine. 2019; 18 (1):1-20.
Chicago/Turabian StyleVidas Raudonis; Agne Paulauskaite-Taraseviciene; Kristina Sutiene; Domas Jonaitis. 2019. "Towards the automation of early-stage human embryo development detection." BioMedical Engineering OnLine 18, no. 1: 1-20.
Increasing resident’s comfort and reducing energy costs have always been two primary objectives of intelligent lighting control systems. It is quite difficult to provide control satisfying the level of individual comfort, sufficient illumination and the energy reduction goals simultaneously. However, finding the balance between resident’s preferred and recommended illumination for the current resident’s activity may be beneficial. This paper addresses the problem of ensuring semi–autonomous assistance in controlling the intensity of light sources. The proposed decision making algorithm allows to provide gradual adaptation to the recommended illumination according the resident’s activity. Resident‘s activity recognition is performed using one of the most popular models of deep learning, such as Convolutional Neural Networks (CNNs).DOI: http://dx.doi.org/10.5755/j01.eie.23.2.17628
Agne Paulauskaite-Taraseviciene; Aiste Stuliene; Egidijus Kazanavicius. Intelligent Lighting Control Providing Semi-Autonomous Assistance. Elektronika ir Elektrotechnika 2017, 23, 68 - 73 .
AMA StyleAgne Paulauskaite-Taraseviciene, Aiste Stuliene, Egidijus Kazanavicius. Intelligent Lighting Control Providing Semi-Autonomous Assistance. Elektronika ir Elektrotechnika. 2017; 23 (2):68 - 73.
Chicago/Turabian StyleAgne Paulauskaite-Taraseviciene; Aiste Stuliene; Egidijus Kazanavicius. 2017. "Intelligent Lighting Control Providing Semi-Autonomous Assistance." Elektronika ir Elektrotechnika 23, no. 2: 68 - 73.
Agne Paulauskaite-Taraseviciene; Nerijus Morkevicius; Vaidas Jukavicius; Laura Kizauskiene; Egidijus Kazanavicius. Agent-Based System Architecture for Intelligent Lighting Control Based on Resident’s Behavior. International Journal of Modeling and Optimization 2015, 5, 48 -54.
AMA StyleAgne Paulauskaite-Taraseviciene, Nerijus Morkevicius, Vaidas Jukavicius, Laura Kizauskiene, Egidijus Kazanavicius. Agent-Based System Architecture for Intelligent Lighting Control Based on Resident’s Behavior. International Journal of Modeling and Optimization. 2015; 5 (1):48-54.
Chicago/Turabian StyleAgne Paulauskaite-Taraseviciene; Nerijus Morkevicius; Vaidas Jukavicius; Laura Kizauskiene; Egidijus Kazanavicius. 2015. "Agent-Based System Architecture for Intelligent Lighting Control Based on Resident’s Behavior." International Journal of Modeling and Optimization 5, no. 1: 48-54.