The Design of Hume: A High-Level Language for the Real-Time Embedded Systems Domain

AbstractThis paper presents results and observations from a survey of 120 industry practitioners in the field of real-time embedded systems. The survey provides insights into the characteristics of the systems being developed today and identifies important trends for the future. It extends the results from the survey data to the broader population that it is representative of, and discusses significant differences between application domains. The survey aims to inform both academics and practitioners, helping to avoid divergence between industry practice and academic research. The value of this research is highlighted by a study showing that the aggregate findings of the survey are not common knowledge in the real-time systems community.

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Problems of synchronization of the PCI devices for the embedded systems in the real time

Proceedings of the 2nd International Conference on Perspective Technologies and Methods in MEMS Design ◽

10.1109/memstech.2006.288678 ◽

2006 ◽

Author(s):

M. Antonyuk ◽

M. Lobur

Keyword(s):

Embedded Systems ◽

Real Time ◽

The Real

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Machine Learning Aided Photonic Diagnostic System for Minimally Invasive Optically Guided Surgery in the Hepatoduodenal Area

Diagnostics ◽

10.3390/diagnostics10110873 ◽

2020 ◽

Vol 10 (11) ◽

pp. 873

Author(s):

Evgeny Zherebtsov ◽

Marina Zajnulina ◽

Ksenia Kandurova ◽

Elena Potapova ◽

Viktor Dremin ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Diagnostic System ◽

Blood Perfusion ◽

Tissue Type ◽

Fibre Optic ◽

Doppler Flowmetry ◽

The Real ◽

Significant Difference ◽

High Level

Abdominal cancer is a widely prevalent group of tumours with a high level of mortality if diagnosed at a late stage. Although the cancer death rates have in general declined over the past few decades, the mortality from tumours in the hepatoduodenal area has significantly increased in recent years. The broader use of minimal access surgery (MAS) for diagnostics and treatment can significantly improve the survival rate and quality of life of patients after surgery. This work aims to develop and characterise an appropriate technical implementation for tissue endogenous fluorescence (TEF) and assess the efficiency of machine learning methods for the real-time diagnosis of tumours in the hepatoduodenal area. In this paper, we present the results of the machine learning approach applied to the optically guided MAS. We have elaborated tissue fluorescence approach with a fibre-optic probe to record the TEF and blood perfusion parameters during MAS in patients with cancers in the hepatoduodenal area. The measurements from the laser Doppler flowmetry (LDF) channel were used as a sensor of the tissue vitality to reduce variability in TEF data. Also, we evaluated how the blood perfusion oscillations are changed in the tumour tissue. The evaluated amplitudes of the cardiac (0.6–1.6 Hz) and respiratory (0.2–0.6 Hz) oscillations was significantly higher in intact tissues (p < 0.001) compared to the cancerous ones, while the myogenic (0.2–0.06 Hz) oscillation did not demonstrate any statistically significant difference. Our results demonstrate that a fibre-optic TEF probe accompanied with ML algorithms such as k-Nearest Neighbours or AdaBoost is highly promising for the real-time in situ differentiation between cancerous and healthy tissues by detecting the information about the tissue type that is encoded in the fluorescence spectrum. Also, we show that the detection can be supplemented and enhanced by parallel collection and classification of blood perfusion oscillations.

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