A low-complexity sub-Nyquist sampling system for wideband Radar ESM receivers

International audienc

Aide au diagnostic de pathologies cardiaques

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Supplementary Material for: Exploring the methodological approaches of studies on radiographic databases used in cariology to feed AI: A Systematic Review

Introduction: A growing number of studies on diagnostic imaging show superior efficiency and accuracy of computer-aided diagnostic systems compared to certified dentists. This methodological systematic review aims to evaluate the different methodological approaches used by studies focusing on machine learning and deep learning and that have used radiographic databases to classify, detect, and segment dental caries. Methods: The protocol was registered in PROSPERO before data collection (CRD42022348097). Literature research was performed in MEDLINE, Embase, IEEE Xplore, and Web of Science until December 2022, without language restrictions. Studies and surveys using a dental radiographic database for the classification, detection, or segmentation of carious lesions were sought. Records deemed eligible were retrieved and further assessed for inclusion by two reviewers who resolved any discrepancies through consensus. A third reviewer was consulted when any disagreements or discrepancies persist between the two reviewers. After data extraction, the same reviewers assessed the methodological quality using the CLAIM and QUADAS-AI checklists. Results: After screening 325 articles, 35 studies were eligible and included. The bitewing was the most commonly used radiograph (n=17) at the time when detection (n=15) was the most explored computer vision task. The sample sizes used ranged from 95 to 38437, while the augmented training set ranged from 300 to 315786. Convolutional neural network (CNN) was the most commonly used model. The mean completeness of CLAIM items was 49 % (SD ± 34%). The applicability of the CLAIM checklist items revealed several weaknesses in the methodology of the selected studies: most of the studies were monocentric, and only 9% of them used an external test set when evaluating the model’s performance. The QUADAS-AI tool revealed that only 43% of the studies included in this systematic review were at low risk of bias concerning the standard reference domain. Conclusion: This review demonstrates that the overall scientific quality of studies conducted to feed AI algorithms is low. Some improvement in the design and validation of studies can be made with the development of a standardized guideline for the reproducibility and generalizability of results and, thus, their clinical applications

Measurement of Instantaneous Shaft Speed by Advanced Vibration Signal Processing - Application to Wind Turbine Gearbox

International audienceCondition monitoring of machines working under non-stationary operations is one of the most challenging problems in maintenance. A wind turbine is an example of such class of machines. One of effective approaches may be to identify operating conditions and investigate their influence on used diagnostic features. Commonly used methods based on measurement of electric current, rotational speed, power and other process variables require additional equipment (sensors, acquisition cards) and software. It is proposed to use advanced signal processing techniques for instantaneous shaft speed recovery from a vibration signal. It may be used instead of extra channels or in parallel as signal verification