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621
Machine learning for bioelectronics on wearable and implantable devices: challenges and potential
Published 2023Get full text
Journal Article -
622
Low carbon economic dispatch of integrated energy systems considering life cycle assessment and risk cost
Published 2023Get full text
Journal Article -
623
Picture This! Social influence in a visually identified YouTube environment
Published 2016Get full text
Final Year Project (FYP) -
624
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625
Micro-engineered perfusable 3D vasculatures for cardiovascular diseases
Published 2017Get full text
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Journal Article -
626
Damage identification by using a self-synchronizing multipoint laser doppler vibrometer
Published 2018Get full text
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Journal Article -
627
Rethinking semi-supervised medical image segmentation: a variance-reduction perspective
Published 2023Conference item -
628
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629
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630
Renewable energy from livestock waste valorization: amyloid-based feather keratin fuel cells
Published 2024Get full text
Journal Article -
631
Ceramicized NASICON-based solid-state electrolytes for lithium metal batteries
Published 2024Get full text
Article -
632
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633
Advanced shape memory technology to reshape product design, manufacturing and recycling
Published 2014Get full text
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Journal Article -
634
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635
Balancing degradability and mechanical strength in keto modified polyethylene through hydrogen bonds
Published 2024Get full text
Journal Article -
636
Sharing plates : cooking and coping with cancer.
Published 2011Get full text
Final Year Project (FYP) -
637
Surface roughness effect on copper–alumina adhesion
Published 2016Get full text
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Journal Article -
638
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639
Poly(methyl methacrylate) for active disassembly
Published 2013Get full text
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Journal Article -
640
Mine yOur owN Anatomy: revisiting medical image segmentation with extremely limited labels
Published 2024“…However, they face three common pitfalls: (1) tailness: medical image data usually follows an implicit long-tail class distribution. Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention. …”
Journal article