OBJECT PERCEPTION IN UNDERWATER ENVIRONMENTS: A SURVEY ON SENSORS AND SENSING METHODOLOGIES

Underwater robots play a critical role in the marine industry. Object perception is the foundation for the automatic operations of submerged vehicles in dynamic aquatic environments. However, underwater perception encounters multiple environmental challenges, including rapid light attenuation, light refraction, or backscattering effect. These problems reduce the sensing devices’ signal-to-noise ratio (SNR), making underwater perception a complicated research topic. This paper describes the state-of-the-art sensing technologies and object perception techniques for underwater robots in different environmental conditions. Due to the current sensing modalities’ various constraints and characteristics, we divide the perception ranges into close-range, medium-range, and long-range. We survey and describe recent advances for each perception range and suggest some potential future research directions worthy of investigating in this field

Development of virtual immunofluorescence images from hematoxylin and eosin-stained images for cancer diagnosis

Haematoxylin and Eosin (H&E) staining is common and viewing these stained images under brightfield microscopes provide basic information of the tumours and other nuclei. In contrast, Immunohistochemical (IHC) images are crucial for cancer diagnosis as it could reveal more information about tumours and its response to treatment. Multiplex Immunofluorescence (mIF), a part of IHC provides a more detailed understanding of the tumour using darkfield microscopy and florescent cameras as opposed to RGB cameras with special monoclonal antibody-based stains. This helps pathologists focus on multiple “biomarkers” or indicators of certain biological processes like immune response. If the same biopsy specimen is used for inspection, the related features obtained from H&E staining and multiplex IF can be utilized to create a Computer Aided Diagnosis (CAD) system including Convolutional Neural Networks which are popularly used in object detection and image segmentation tasks. The study is divided into two parts, automated optical flow-based image registration and CD3 (biomarker for T cells) region prediction using a special type of a convolutional neural network called as generative adversarial networks (GANs). Concepts of optical flow, k-means clustering, and Otsu thresholding are combined to create a faster and robust intensity-based image registration pipeline, in which the DAPI (4′,6-diamidino-2-phenylindole) channel of the mIF image is co-registered with the corresponding H&E image, following which the other channels in mIF image are transformed to match the registration. Finally, the CD3 channel image is superimposed with the matching H&E image to create the reference image needed for deep learning. Two variations of GAN, the Pix2Pix GAN and cycleGAN models are modified to work with the registered image dataset to predict CD3 regions. As mIF images are available only by using expensive and complex machines, inexpensive and easy to obtain H&E images can now be used in conjunction with GAN models to obtain similar data, which could significantly reduce the costs of cancer treatment since this method not only helps in getting multi-modal image data based on only one type of image, but it also helps in making cancer immunotherapy, a form of cancer treatment dependent on these images mainstream.Master of Engineerin

Low-cost underwater localisation using single-beam echosounders and inertial measurement units

Underwater robot localisation is challenging as it cannot rely on sensors such as the GPS due to electromagnetic wave attenuation or optical cameras due to water turbidity. SONARs are immune to these issues, hence they are used as alternatives for underwater navigation despite lower spatial and temporal resolution. Single-beam SONARs are sensors whose main output is distance. When combined with a filtering algorithm like the Kalman filter, these distance readings can correct localisation data obtained by inertial measurement units. Compared to multi-beam imaging SONARs, the single-beam SONARs are inexpensive to integrate into underwater robots. Therefore, this study aims to develop a low-cost localisation solution utilizing single-beam SONARs and pressure-based depth sensors to correct dead-reckoning linear localisation data using Kalman filters. From experiments, a single-beam SONAR per degree of freedom was able to correct localisation data, without the need of complicated data fusion methods.Nanyang Technological UniversitySubmitted/Accepted versionThis paper is done as part of the work conducted under the SAAB-NTU Joint Lab with support from SAAB Singapore Pte. Ltd, SAAB AB, NTU Robotics Research Centre (RRC) and NTU Sports and Recreational Centre (NTU-SRC)

A CNN-based auto-segmentation pipeline for breast cancer imaging

No abstract available

Investigation of virtual & augmented reality classroom learning environments in university STEM education

This study investigates the use of virtual and augmented reality (VAR) in higher education on students’ self-efficacy in science and engineering, and academic performance. An innovative VAR classroom is set up as an immersive and interactive learning environment at Nanyang Technological University for curriculum-based learning. This classroom is equipped with 6 individual VR displays and headsets, where high-end computers are connected to each display providing the computational power required for VAR technology. 46 undergraduate engineering students experience the VAR lessons over the course of 5 weeks. The self-efficacy and academic performance of the students are measured for quantitative analysis with surveys and regular (pre-/post-) tests as they participated in the lessons. Post-lesson focus-group discussions and interviews are conducted for qualitative analysis. The data analysis shows a significant increase in students’ self-efficacy levels and academic performance after 5 weeks of VAR lessons. It is found that the collaborative, interactive and immersive nature of the VAR learning environment enriches students’ learning experiences and thus contributes to their learning effectiveness. The study provides insights into how the VAR learning environment affects undergraduate students’ learning, and how the VAR system can be deployed for effective STEM education.Ministry of Education (MOE)This research is supported by the Ministry of Education, Singapore, under its Tertiary Research Fund (Project MOE2018-TRF-006)

Cohort profile: the ESC EURObservational Research Programme Non-ST-segment elevation myocardial infraction (NSTEMI) Registry

Aims The European Society of Cardiology (ESC) EURObservational Research Programme (EORP) Non-ST-segment elevation myocardial infarction (NSTEMI) Registry aims to identify international patterns in NSTEMI management in clinical practice and outcomes against the 2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without ST-segment-elevation. Methods and results Consecutively hospitalised adult NSTEMI patients (n = 3620) were enrolled between 11 March 2019 and 6 March 2021, and individual patient data prospectively collected at 287 centres in 59 participating countries during a two-week enrolment period per centre. The registry collected data relating to baseline characteristics, major outcomes (inhospital death, acute heart failure, cardiogenic shock, bleeding, stroke/transient ischaemic attack, and 30-day mortality) and guideline-recommended NSTEMI care interventions: electrocardiogram pre- or in-hospital, prehospitalization receipt of aspirin, echocardiography, coronary angiography, referral to cardiac rehabilitation, smoking cessation advice, dietary advice, and prescription on discharge of aspirin, P2Y12 inhibition, angiotensin converting enzyme inhibitor (ACEi)/angiotensin receptor blocker (ARB), beta-blocker, and statin. Conclusion The EORP NSTEMI Registry is an international, prospective registry of care and outcomes of patients treated for NSTEMI, which will provide unique insights into the contemporary management of hospitalised NSTEMI patients, compliance with ESC 2015 NSTEMI Guidelines, and identify potential barriers to optimal management of this common clinical presentation associated with significant morbidity and mortality