Le PNUD et la sécurité humaine

International audienc

3D object classification in baggage computed tomography imagery using randomised clustering forests

We investigate the feasibility of a codebook approach for the automated classification of threats in pre-segmented 3D baggage Computed Tomography (CT) security imagery. We compare the performance of five codebook models, using various combinations of sampling strategies, feature encoding techniques and classifiers, to the current state-of-the-art 3D visual cortex approach [1]. We demonstrate an improvement over the state-of-the-art both in terms of accuracy as well as processing time using a codebook constructed via randomised clustering forests [2], a dense feature sampling strategy and an SVM classifier. Correct classification rates in excess of 98% and false positive rates of less than 1%, in conjunction with a reduction of several orders of magnitude in processing time, make the proposed approach an attractive option for the automated classification of threats in security screening settings

Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions

BackgroundTargeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing.ResultsAll panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5-20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden.ConclusionThis comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.Peer reviewe

Evolutionary Optimization of Robust and Chattering-Free Mamdani Type Fuzzy Controller

In fuzzy control area, the evolutionary algorithm is one of the most common design tools for fuzzy knowledge base generation. In this paper, we present the application of an integer evolutionary algorithm (IEA) for simultaneous optimization of fuzzy rule base and fuzzy data base of Mamdani-type fuzzy controller. The motivation behind this work is to design a robust and accurate controller without chattering phenomenon in the control input. More specifically, we consider the minimization of the variance of the control input in the same time as root mean square tracking error during the optimization. This fact leads the IEA to search for accurate fuzzy controller that provides just enough control input for smooth behavior. To assess the design technique, simulations were conducted with direct-drive DC motor. The simulation results show the effectiveness of the proposed IEA in designing a robust and chattering-free Mamdani fuzzy controller with high accuracy as compared to a conventional PD controller

A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery

We present an experimental comparison of 3D feature descriptors with application to threat detection in Computed Tomography (CT) airport baggage imagery. The detectors range in complexity from a basic local density descriptor, through local region histograms and three-dimensional (3D) extensions to both to the RIFT descriptor and the seminal SIFT feature descriptor. We show that, in the complex CT imagery domain containing a high degree of noise and imaging artefacts, a specific instance object recognition system using simpler descriptors appears to outperform a more complex RIFT/SIFT solution. Recognition rates in excess of 95% are demonstrated with minimal false-positive rates for a set of exemplar 3D objects