DEI

Advanced Computing Paradigms

Submitted by zennaro2 on Thu, 12/10/2015 - 12:04

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Design and development of optimized applications for science and engineering

Submitted by zennaro2 on Thu, 12/10/2015 - 12:04

To harness the performance potential of today's complex computing platforms, applications have to be optimized in several dimensions, such as number of operations, parallelism, access to the memory system, communication among processing nodes. Computer science methodologies are fruitfully combined with domain specific knowledge in various areas of scientific and technical computing. Examples:

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Human Movement Bioengineering

Submitted by zennaro2 on Thu, 12/10/2015 - 12:02

The central research area of the research group ‘human movement bioengineering’ is to investigate the mechanics of musculoskeletal function during differentactivities (gait analysis, posture and balance, sports activities) and how different pathologies (i.e. diabetes, Parkinson’s Disease, Fragile X syndrome, ACL injury, temporomandibular joint disfunction) impact on the musculoskeletal function.

Homepage: http://biomov.dei.unipd.it

People:

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Human Movement Bioengineering

Submitted by zennaro2 on Thu, 12/10/2015 - 12:02

Developing biomechanical tools which allow a better understanding of the impact of training programs or clinical interventions on people's suffering of diabetes, stroke, rheumatic diseases, Parkinson's disease, flat or cavus foot, maxillary transverse discrepancy, fragile X syndrome
Developing markerless motion capture software application able to collect data also underwater
Developing systems for simultaneous acquisition of motion capture, ground reaction forces and plantar pressure data during gait

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Biomedical Image Analysis

Submitted by zennaro2 on Thu, 12/10/2015 - 12:00

This research area focuses on novel techniques for biomedical image processing and analysis, and on the development of diagnostic tools related to this field. It deals with image enhancement techniques, detection and segmentation of anatomical structures and lesions, identification of clinical parameters related to health state and their estimation. The developed techniques range from novel image enhancement and pre-processing methods to advanced machine learning and deep learning techniques for image classification and segmentation.

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Image processing and analysis in ophthalmology

Submitted by zennaro2 on Thu, 12/10/2015 - 12:00

Corneal nerve images from confocal microscopy: segmentation of nerve structures, estimation of clinical parameters, such as tortuosity, nerve density, etc. Composition of several images into large mosaic images.

Corneal epithelium images from confocal and specular microscopy: segmentation of cell contours, estimation of clinical parameters cell density, pleomorphism, polymegethism.